La finalidad de este apartado es realizar un análisis exploratorio de los datos para entender la distribución de las características y su relación con la supervivencia, así como identificar patrones y relaciones entre las variables, a través de pruebas estadísticas o técnicas de selección de variables. También se pretenden seleccionar las variables clínicas y genéticas más relevantes para la predicción de la supervivencia.
En primer lugar, importamos las liberarías necesarias para explorar los datos, así como también definimos las rutas de los ficheros.
import os
import glob
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
from lazyme.string import color_print
pd.options.mode.chained_assignment = None
pd.set_option('display.max_columns', None)
warnings.simplefilter(action='ignore', category=FutureWarning)
import matplotlib.pyplot as plt
from lifelines import *
from sklearn.preprocessing import StandardScaler
from matplotlib_venn import venn3
from scipy import stats
from sklearn.decomposition import PCA
from sklearn import preprocessing
global_path = "/Users/alba/Desktop/TFM"
imatges_path = glob.glob(f"{global_path}/data/imatges/*")
taules_path = f"{global_path}/data/taules"
# cargamos el conjunto de datos
raw_df_nki = pd.read_csv(f"{taules_path}/NKI.csv", low_memory=False)
# eliminamos las características que no aportan información para el estudio en cuestión
drop_features_nki = ['Patient','ID','barcode']
raw_df_nki.drop(drop_features_nki,axis=1,inplace=True)
# visualizamos las primeras filas del conjunto de datos
raw_df_nki.head()
| age | eventdeath | survival | timerecurrence | chemo | hormonal | amputation | histtype | diam | posnodes | grade | angioinv | lymphinfil | esr1 | G3PDH_570 | Contig45645_RC | Contig44916_RC | D25272 | J00129 | Contig29982_RC | Contig56678_RC | Contig53047_RC | Contig19551 | Contig47230_RC | Contig46501_RC | Contig20749_RC | AL157500 | AL157502 | Contig37376_RC | Contig45395_RC | X98307 | AL157505 | AB033027 | Contig24026_RC | Contig27800_RC | Contig42500_RC | Contig7147 | Contig32037_RC | Contig16374_RC | Contig42349_RC | Contig33976_RC | Contig49874_RC | AB033060 | U45975 | M34428 | AB033083 | NM_003004 | Contig7192 | Contig23039_RC | Contig22685_RC | D84140 | Contig43806_RC | NM_003022 | Contig54847_RC | Contig33260_RC | NM_002300 | Contig13929_RC | NM_002318 | NM_003058 | Contig66143_RC | Contig44519_RC | NM_001609 | Contig17206_RC | NM_001611 | NM_002341 | NM_001612 | NM_002343 | NM_001615 | NM_002345 | NM_003081 | NM_001627 | NM_002358 | Contig50838_RC | NM_000900 | NM_000903 | NM_001635 | NM_000909 | NM_000912 | NM_001645 | NM_001647 | Contig35896_RC | NM_000926 | NM_001657 | NM_000930 | NM_000931 | NM_000936 | NM_002395 | NM_001674 | NM_001676 | NM_001677 | X04706 | D25328 | Contig37076 | Contig44366 | NM_000959 | Contig46463_RC | Contig5443_RC | NM_001692 | NM_000963 | NM_000964 | Contig50122_RC | Contig25711_RC | Contig33121_RC | Contig12593_RC | U22027 | U22028 | U22029 | AF041410 | Contig15750_RC | Contig26182_RC | Contig22423_RC | Contig15133_RC | X99142 | U46752 | X72308 | Contig25195_RC | Contig47045_RC | Contig42789_RC | Contig27623_RC | Contig27014_RC | NM_003104 | Contig56386_RC | Contig46909_RC | NM_003108 | Contig41360_RC | Contig39385_RC | NM_003118 | Contig52018_RC | Contig43645_RC | NM_002407 | Contig20600_RC | NM_002411 | Contig52994_RC | NM_002416 | Contig9541_RC | Contig21930_RC | Contig50927_RC | NM_002421 | NM_002422 | NM_002425 | NM_002426 | NM_003155 | NM_002427 | Contig46802_RC | NM_001710 | NM_002443 | NM_001717 | NM_002449 | NM_001723 | NM_003181 | NM_001725 | NM_002456 | NM_002457 | NM_001732 | NM_002462 | NM_001734 | NM_001740 | NM_002478 | NM_002483 | NM_001756 | NM_001758 | Contig30045_RC | NM_001764 | Contig21063_RC | Contig18502_RC | Contig33811_RC | Contig37562_RC | Contig36440 | NM_001786 | Contig15329_RC | Contig38517_RC | NM_001793 | Contig7809_RC | NM_001797 | AF090920 | Contig54547_RC | Contig14967_RC | Contig61829_RC | Contig35765 | Contig35237_RC | AW025980_RC | AL133038 | Contig34258_RC | Contig41548_RC | AF201951 | AB041269 | AL133074 | Contig50288_RC | AL133086 | AL133087 | Contig8165_RC | Contig33888_RC | Contig53445_RC | Contig40712_RC | Contig23711_RC | Contig29755_RC | NM_003206 | NM_003225 | NM_003226 | NM_003234 | NM_003236 | NM_002509 | NM_003239 | NM_002515 | NM_003246 | NM_003247 | NM_003248 | Contig44439_RC | NM_002521 | NM_003256 | NM_003258 | Contig43476_RC | NM_001809 | NM_001814 | NM_002544 | NM_002546 | NM_001819 | NM_001823 | NM_003283 | NM_003287 | Contig22716_RC | NM_001831 | NM_003294 | NM_001838 | Contig52960_RC | Contig45670_RC | Contig16756_RC | NM_002570 | NM_001844 | NM_002575 | L11645 | Contig15418_RC | NM_001853 | NM_001854 | NM_002590 | NM_002591 | Contig21136_RC | Contig29015_RC | Contig40196_RC | NM_001870 | NM_001871 | NM_001872 | NM_001873 | NM_001877 | NM_001878 | NM_001882 | Contig28528 | NM_001884 | NM_001885 | NM_001888 | Contig51981_RC | Contig42624_RC | Contig40789_RC | Contig28549 | AA600139_RC | Contig44909_RC | Contig49532_RC | Contig39678_RC | Contig28028_RC | Contig21021_RC | Contig31333_RC | Contig47832_RC | U47671 | Contig33376_RC | Contig30955_RC | Contig14683_RC | Contig41530_RC | Contig38826_RC | NM_020038 | Contig44544_RC | NM_004003 | Contig8839_RC | NM_004010 | U05589 | Contig29828_RC | Contig15531_RC | NM_003311 | NM_012067 | Contig5710_RC | Contig53411_RC | NM_002614 | NM_002620 | Contig54058 | NM_002628 | NM_003358 | NM_003359 | NM_004088 | AB007936 | Contig25593_RC | NM_001901 | NM_002639 | NM_003376 | NM_003378 | AB007954 | NM_001920 | NM_002652 | NM_001926 | NM_001928 | Contig21225_RC | R70506_RC | NM_003392 | NM_002666 | Contig57138_RC | NM_001942 | NM_001944 | Contig36761_RC | Contig48156_RC | Contig16595_RC | NM_001956 | NM_002686 | NM_001958 | Contig31295_RC | NM_001964 | NM_002697 | Contig56167_RC | NM_001979 | Contig52182_RC | NM_001990 | NM_001993 | NM_001999 | Contig35977 | Contig35978 | Contig9681_RC | Contig53022_RC | Contig46942_RC | Contig26310_RC | Contig46934_RC | Contig22408_RC | Contig28970_RC | NM_012101 | Contig46918_RC | NM_020130 | NM_004100 | X66087 | NM_004114 | Contig53357_RC | NM_020162 | NM_020163 | Contig21187_RC | NM_004131 | NM_020178 | NM_003412 | NM_004143 | NM_003430 | NM_002705 | NM_002711 | NM_004170 | NM_004171 | Contig27967_RC | NM_004176 | NM_004181 | NM_004185 | NM_004190 | NM_003462 | NM_002737 | NM_003474 | Contig44260_RC | Contig46553_RC | NM_003489 | Contig37571_RC | NM_002763 | NM_002774 | NM_002775 | Contig53226_RC | Contig21665_RC | Contig16202_RC | S70004 | Contig20686_RC | Contig8898_RC | U64564 | S62027 | Contig31862_RC | Contig34884_RC | Contig2237_RC | Contig1508_RC | Contig48715_RC | Contig48106_RC | Contig37898_RC | Contig30516_RC | Contig48707_RC | Contig27120_RC | Contig30274_RC | Contig33296_RC | AF035288 | Contig31596_RC | NM_020215 | Contig38746_RC | Contig19452_RC | Contig13866_RC | Contig16422_RC | NM_004203 | NM_020244 | NM_004207 | Contig29147_RC | NM_004213 | NM_012242 | Contig45786_RC | AB040886 | AB018289 | NM_003500 | NM_003506 | NM_003508 | NM_003512 | NM_004244 | NM_003516 | NM_004245 | NM_012269 | NM_003520 | NM_004252 | NM_002800 | Contig53506 | NM_004265 | NM_002809 | NM_003538 | NM_002820 | NM_003561 | NM_004291 | NM_002844 | NM_003578 | Contig28670_RC | NM_002852 | NM_002854 | NM_002855 | Contig53307_RC | NM_002864 | Contig52945_RC | NM_002888 | AF172932 | NM_002899 | Contig36064_RC | Contig15169_RC | Contig38500_RC | Contig34372_RC | Contig44870 | Contig50628_RC | Contig50019_RC | Contig6212_RC | Contig45397_RC | AF052090 | AF052095 | Contig34957_RC | Contig43096_RC | Contig45389_RC | Contig50979_RC | Contig15038_RC | NM_021012 | NM_021015 | AB040900 | AB018305 | AL117406 | Contig39556_RC | AB018311 | AL117418 | AB040923 | AB040926 | AB040930 | NM_005010 | AB018345 | NM_005025 | NM_012319 | NM_021069 | Contig40552_RC | AB040957 | AL117452 | NM_021076 | X56807 | Contig28866_RC | NM_004315 | NM_005046 | NM_012337 | NM_012339 | AB032953 | NM_012342 | AB032962 | Contig30833_RC | M27749 | Contig42228_RC | Contig27294_RC | NM_020372 | NM_005063 | NM_020373 | NM_004335 | NM_004336 | NM_005067 | NM_005069 | Contig56434_RC | Contig44287_RC | NM_003613 | NM_003615 | NM_004345 | NM_003617 | NM_004351 | NM_005080 | NM_004354 | NM_005084 | NM_004358 | NM_004360 | NM_004361 | NM_004362 | NM_004369 | Contig31546_RC | NM_003641 | NM_003645 | NM_004374 | NM_004378 | NM_012397 | Contig18296_RC | Contig45600 | NM_002923 | NM_003652 | NM_004385 | NM_003657 | NM_002932 | NM_004392 | Contig47077 | AJ270996 | Contig27405_RC | NM_002963 | NM_002964 | NM_002965 | Contig23420_RC | U80736 | Contig37483_RC | Contig43435_RC | Contig38438_RC | NM_002983 | NM_002984 | NM_002985 | NM_002986 | NM_002988 | NM_002989 | NM_002996 | NM_002997 | U56725 | Contig54603_RC | AF052176 | Contig32136_RC | Contig29699 | Contig6051_RC | Contig11142_RC | Contig9229 | Contig50360_RC | Contig27032_RC | Contig53007_RC | Contig57359_RC | NM_021127 | NM_021136 | NM_021139 | NM_005101 | NM_020411 | AL133566 | NM_021147 | Contig442_RC | Contig25090_RC | Contig52629_RC | Contig28947_RC | Contig1063_RC | AL117553 | NM_005130 | NM_005132 | NM_004405 | NM_004406 | NM_005139 | NM_012429 | NM_004415 | NM_004417 | Contig43639_RC | NM_012445 | NM_004430 | NM_005165 | NM_020477 | Contig28697_RC | NM_003714 | NM_012467 | NM_004448 | Contig54425 | NM_005181 | NM_003725 | NM_004456 | NM_005187 | NM_004460 | Contig43998_RC | NM_005196 | Contig20953_RC | Contig59287_RC | NM_003740 | NM_004472 | Contig50822_RC | NM_004482 | NM_004484 | NM_004490 | NM_004496 | Contig50814_RC | NM_003785 | NM_003786 | NM_003787 | NM_003790 | Contig38285_RC | Contig6848_RC | Contig5403_RC | AF131741 | Contig9755_RC | AF131756 | Contig40829_RC | AF131770 | Contig39492_RC | AF131784 | AL133619 | U06715 | AL133644 | AL117612 | NM_013230 | NM_013231 | NM_005213 | AL117638 | NM_005218 | NM_013253 | NM_005235 | AF176012 | NM_004522 | NM_005252 | NM_004525 | X16302 | NM_003806 | Contig47220 | Contig52970_RC | NM_005288 | Contig19179_RC | NM_003832 | Contig54536 | NM_004566 | NM_003843 | Contig38983_RC | NM_004585 | NM_003862 | NM_004594 | Contig59870_RC | NM_003878 | Contig41905_RC | NM_003881 | Contig46567 | NM_003888 | NM_003890 | NM_003897 | Contig15313_RC | Contig19064_RC | Contig28038_RC | Contig51967_RC | Contig37873 | AF131851 | Contig35814_RC | Contig45291_RC | Contig43833_RC | Contig55228_RC | Contig1850_RC | NM_014004 | Contig20629_RC | NM_006006 | AF026941 | NM_006017 | Contig14812_RC | NM_006025 | AL109706 | NM_013324 | Contig46597_RC | Contig16262_RC | NM_005310 | X57809 | NM_005319 | NM_005320 | X57819 | NM_005325 | NM_014073 | NM_014074 | NM_005326 | NM_020639 | NM_014086 | NM_013357 | NM_005342 | NM_006071 | NM_006073 | NM_013363 | NM_004616 | NM_020659 | U83115 | NM_013372 | NM_006086 | NM_005357 | NM_005362 | NM_020672 | NM_006096 | NM_005368 | Contig35298_RC | NM_005375 | AJ249377 | NM_005382 | Contig7755_RC | AL109791 | NM_004664 | NM_003937 | NM_005396 | NM_005398 | NM_004670 | NM_004684 | Contig51463_RC | NM_004694 | NM_004695 | NM_004696 | NM_003975 | NM_003979 | Contig54477_RC | NM_003981 | NM_003986 | Contig27900_RC | Contig48043_RC | Contig39670_RC | Contig53641_RC | Contig27749_RC | Contig50603_RC | Contig54354_RC | Contig9810_RC | Contig37946_RC | Contig3607_RC | NM_014112 | NM_006103 | NM_006107 | NM_006113 | Contig8222_RC | NM_006115 | Contig53968_RC | NM_013409 | Contig51066_RC | NM_013410 | NM_006121 | M29540 | Contig35629_RC | NM_013421 | NM_005408 | NM_005409 | NM_006138 | Contig20603_RC | NM_005410 | X82693 | NM_006142 | Contig46204_RC | NM_006157 | NM_006159 | NM_004701 | NM_004702 | Contig11613_RC | NM_004711 | NM_004734 | NM_006198 | AF000990 | Contig40368_RC | NM_005478 | NM_004750 | AJ223352 | AJ223353 | NM_004774 | Contig23502_RC | NM_004780 | Contig66705_RC | NM_004792 | NM_004796 | X07834 | Contig56143_RC | Contig11097_RC | Contig45703_RC | Contig31864_RC | J02639 | Contig38171_RC | AF116715 | Contig17411_RC | Contig33899_RC | Contig44708_RC | Contig43136_RC | Contig46301_RC | NM_014211 | NM_014214 | Contig42774_RC | NM_006206 | NM_006207 | Contig38040_RC | Contig54913_RC | NM_014241 | NM_006226 | NM_014246 | NM_006235 | NM_014258 | NM_006240 | NM_005514 | NM_005518 | Contig47381_RC | NM_014274 | Contig50769_RC | Contig47982_RC | NM_005532 | NM_004807 | NM_014286 | NM_005544 | NM_006274 | Contig57076_RC | Contig48328_RC | NM_005558 | NM_004833 | NM_005564 | NM_004835 | Contig16759_RC | NM_005573 | Contig20793_RC | NM_005586 | Contig14458_RC | NM_004864 | NM_004867 | Contig3682_RC | NM_004878 | Contig52947_RC | NM_004887 | Contig48806_RC | Contig29369_RC | AL353944 | J04162 | Contig56583_RC | Contig37890_RC | AB029018 | Contig55838_RC | Contig39100_RC | Contig54867_RC | Contig11648_RC | NM_007019 | Contig51595_RC | NM_014321 | NM_007036 | NM_007038 | Contig56307 | NM_006334 | NM_006338 | NM_007069 | NM_006344 | NM_014365 | NM_014373 | NM_007088 | Contig37743_RC | NM_006379 | NM_014398 | NM_014399 | Contig28152_RC | NM_004920 | NM_004923 | AK000004 | Contig33750_RC | NM_020974 | NM_006398 | Contig40889_RC | NM_005672 | NM_004950 | Contig49093 | NM_020990 | NM_020994 | NM_005688 | Contig30569_RC | NM_004963 | Contig11275_RC | U66702 | AF079529 | AK000060 | NM_004986 | NM_004988 | Contig33726_RC | NM_004994 | Contig50719_RC | Contig43195_RC | X93006 | AL080059 | AL080079 | Contig60315_RC | Contig44652_RC | AL080094 | Contig38918_RC | Contig46937_RC | Contig1789_RC | AL110126 | Contig57270_RC | AL110152 | NM_007105 | Contig1682_RC | AL110168 | NM_014405 | NM_007117 | Contig40026_RC | AL110171 | AL110181 | NM_006408 | AB002351 | NM_006419 | Contig51202_RC | NM_014443 | X66945 | NM_007168 | AF097021 | NM_007173 | NM_005727 | NM_014479 | M29873 | M29874 | NM_006461 | NM_005733 | NM_007191 | Contig41850_RC | Contig52891_RC | NM_006475 | NM_005749 | AK000106 | NM_005764 | AJ225092 | AJ225093 | NM_005794 | Contig56160_RC | AK000168 | Contig51896_RC | Contig55181_RC | AL080137 | Contig40238_RC | Contig32594_RC | NC_001807 | Contig67169_RC | AF101051 | Contig30519_RC | AF222694 | AL080199 | Contig6118_RC | AL110202 | Contig41453_RC | AB011146 | Contig31133_RC | AL110252 | Contig6612_RC | Contig51749_RC | AI497657_RC | NM_007231 | Contig21406_RC | Contig28179_RC | Contig42759_RC | NM_007253 | NM_006528 | NM_006529 | Contig18611_RC | NM_006533 | NM_014553 | NM_006536 | Contig50913_RC | Contig31476_RC | Contig49058_RC | NM_006551 | NM_007281 | NM_005824 | Contig31010_RC | NM_005832 | NM_014585 | Contig32798_RC | NM_005842 | D86974 | NM_005855 | Contig57293 | Contig753_RC | AF055033 | J05125 | Contig10601_RC | Contig30390_RC | AL080222 | AF055084 | AL080235 | J05175 | Contig32667_RC | M63438 | Contig23211_RC | Contig24541_RC | NM_016002 | Contig53296_RC | NM_015310 | NM_007315 | NM_007333 | NM_006623 | NM_014668 | Contig44191_RC | Contig54729_RC | Contig47439_RC | NM_014681 | NM_005940 | NM_005941 | NM_014696 | NM_005950 | NM_006681 | Contig57389 | NM_013982 | Contig64688 | NM_013989 | NM_005971 | Contig33126_RC | NM_005978 | AF144054 | NM_013999 | NM_005980 | NM_005982 | Contig57644_RC | AK000345 | AF224266 | Contig28030_RC | AF053712 | Contig36499_RC | Contig40158_RC | U67784 | AL137274 | NM_016109 | Contig51685_RC | Contig42328_RC | Contig44530_RC | NM_016140 | NM_015417 | Y16132 | Contig29822_RC | Contig31646_RC | NM_014723 | NM_006705 | NM_006727 | Contig20355_RC | Contig48400_RC | Y14737 | NM_006744 | NM_014767 | Contig14996_RC | NM_006751 | AK001101 | NM_014781 | NM_006765 | NM_006769 | NM_014790 | T50661_RC | NM_006787 | Contig35875_RC | Contig57725_RC | Contig37702_RC | AF070536 | Contig47405_RC | K02276 | AK000451 | Contig399_RC | Contig52737_RC | AL137334 | AL137342 | AL137343 | Contig3464_RC | X52015 | Contig38901_RC | Contig31142_RC | M54927 | Contig44010_RC | Contig48971_RC | NM_016220 | NM_015507 | NM_015515 | NM_016249 | NM_016260 | NM_016267 | Contig23466_RC | Contig53944_RC | NM_006804 | NM_006820 | NM_006823 | NM_006829 | NM_006845 | NM_006847 | Contig43983_RC | NM_014875 | Contig52957_RC | NM_006868 | Contig38520_RC | Contig51994_RC | NM_014897 | NM_014899 | Contig34009_RC | AJ224741 | Contig57595 | Contig32192 | Contig30016_RC | AF070632 | Contig51369_RC | Contig39834_RC | Contig14954_RC | Contig10961_RC | Contig21619_RC | Contig16531_RC | AL137449 | Contig30260_RC | AL049257 | AL049265 | Contig38589_RC | AL049279 | Contig55725_RC | NM_016348 | NM_016352 | NM_016358 | NM_016359 | NM_014903 | NM_016364 | Contig47456_RC | Contig21847_RC | AB028974 | Contig57903_RC | Z11887 | AK002005 | Contig57631 | AK002016 | Contig48913_RC | K02403 | Contig57653_RC | Contig30480_RC | X01394 | AK002088 | Contig34449_RC | Contig46362_RC | AK000660 | Contig54365_RC | Contig56801_RC | AL137517 | AL137540 | Contig19384_RC | Contig41983_RC | AL049337 | Contig36520_RC | AB037734 | AL137566 | AL137567 | Contig51220_RC | AB037745 | Contig5804_RC | Contig440 | AB037763 | NM_015719 | AB037791 | NM_016459 | AF179224 | NM_016471 | Contig44265_RC | AK002107 | AF064200 | AK002138 | Contig53598_RC | AK001423 | Contig47781_RC | Contig46435_RC | Contig49342_RC | Contig51775_RC | AL137619 | Contig13879_RC | AL049423 | Contig56390_RC | AB037821 | AB037836 | AL137669 | Contig23108_RC | AB037848 | Contig51660_RC | AF005487 | AL137698 | X51630 | Contig16786_RC | NM_016569 | NM_016577 | AL359053 | AL359062 | Contig24682_RC | Contig15325_RC | Contig39242_RC | Contig2339_RC | Contig49855 | Contig39226_RC | Contig30384_RC | Contig46508_RC | Contig45537_RC | Contig51255_RC | Contig42882_RC | Contig3313 | Contig49790_RC | NM_018004 | AL137725 | NM_018014 | Contig47106_RC | NM_018043 | AL137761 | Contig41887_RC | NM_016619 | Contig42751_RC | Contig41413_RC | Contig53183_RC | NM_016640 | X52486 | AJ275978 | Contig40434_RC | X03084 | Contig30481_RC | Contig42011_RC | Contig4380_RC | Contig20137_RC | Contig58260_RC | Contig45511_RC | Contig50357_RC | AF020919 | Contig46089_RC | NM_018104 | Contig38580_RC | NM_018136 | NM_000028 | NM_000029 | NM_017414 | Contig15384_RC | NM_000037 | NM_017422 | NM_017423 | Contig52543_RC | NM_000044 | NM_018166 | Contig14647_RC | NM_000050 | NM_000067 | NM_017459 | AB020689 | NM_000077 | Contig64297_RC | NM_000089 | Contig27882_RC | NM_000090 | NM_000095 | NM_000096 | Contig49589_RC | Contig57142_RC | Contig46452_RC | U79293 | U79299 | Contig41587_RC | AF073299 | AF047826 | Contig60157_RC | AF103375 | AF007150 | AF007153 | NM_018208 | NM_018215 | Contig35030_RC | NM_000104 | Contig39616_RC | M30818 | NM_000111 | NM_000125 | NM_000129 | NM_000141 | Contig47994_RC | NM_018265 | NM_016817 | NM_000165 | NM_000168 | NM_000169 | Contig54667_RC | Contig29647_RC | Contig42139 | NM_000187 | Contig55997_RC | NM_017579 | NM_000196 | NM_009585 | NM_009588 | Contig1018_RC | Contig43368_RC | Contig41676_RC | Contig42532_RC | AF058075 | Contig43253_RC | AF063936 | Contig42274_RC | Contig32563_RC | AF103458 | Z48633 | Contig23581_RC | NM_019000 | Contig33284_RC | NM_019013 | Contig28888_RC | Contig375_RC | NM_018306 | D90070 | NM_019049 | NM_019058 | NM_000222 | NM_000224 | NM_000228 | NM_000230 | NM_000237 | NM_000238 | NM_000239 | NM_000240 | Contig51117_RC | NM_000266 | NM_000269 | Contig42227 | NM_000295 | NM_000299 | NM_017680 | AF234532 | Contig65658_RC | Contig48800_RC | Contig54968_RC | Contig37501_RC | Contig24609_RC | Contig48518_RC | NM_018407 | Contig53281_RC | NM_018410 | NM_018422 | AL049932 | NM_000320 | NM_000323 | NM_000324 | NM_000331 | NM_001062 | NM_001063 | NM_001073 | NM_001074 | Contig1805_RC | NM_000346 | NM_001076 | NM_001078 | AL049963 | NM_017731 | NM_000353 | NM_001085 | NM_000358 | NM_001089 | NM_018476 | NM_000362 | NM_000363 | AL049987 | Contig24252_RC | NM_000370 | L27560 | NM_000381 | NM_000393 | NM_000396 | NM_000399 | NM_017786 | Contig56765_RC | AF113007 | X02761 | M90657 | Contig16453_RC | AB023144 | Contig54325_RC | NM_001102 | NM_001109 | Contig38654_RC | NM_001124 | NM_001129 | Contig32602_RC | NM_001144 | NM_018530 | NM_000421 | NM_001150 | NM_000422 | NM_000424 | Contig13300_RC | Contig43026_RC | NM_000439 | NM_001168 | NM_017821 | Contig56503_RC | Contig51037_RC | NM_001185 | NM_001189 | NM_017852 | Contig57825_RC | NM_000477 | NM_017870 | Contig32336_RC | NM_017878 | NM_000493 | AW518944_RC | NM_000495 | Contig34395_RC | U96394 | Contig55883_RC | Contig30995_RC | Contig41804_RC | Contig54295_RC | AB023211 | U28831 | M33318 | Contig54414_RC | Contig41538_RC | Contig1239_RC | Contig42402_RC | NM_001203 | AB014533 | AB014534 | NM_001216 | NM_001218 | NM_000507 | NM_000509 | NM_000518 | Contig43708_RC | NM_001254 | L10333 | NM_001267 | NM_018653 | NM_001275 | NM_001276 | Contig2446_RC | Contig50529 | NM_017954 | NM_017957 | Contig15190_RC | NM_000582 | NM_000584 | Contig13724_RC | NM_000592 | NM_000597 | NM_000599 | Contig42582 | Contig21493_RC | Contig54993_RC | Contig58512_RC | Contig4382_RC | AL050202 | AL050227 | AF111849 | NM_002001 | NM_002023 | NM_001306 | NM_002036 | NM_002038 | NM_002048 | AB006625 | NM_002051 | NM_002053 | NM_001327 | NM_000600 | NM_001333 | NM_000607 | NM_001336 | NM_001338 | NM_001353 | NM_000624 | NM_002089 | Contig30213_RC | Contig33235_RC | NM_000633 | NM_002091 | NM_001362 | NM_000636 | Contig5549_RC | NM_000642 | NM_000646 | Contig51558_RC | NM_001387 | NM_000662 | NM_001393 | NM_001394 | NM_001395 | NM_000667 | NM_000668 | Contig693_RC | NM_000685 | NM_000693 | NM_000695 | Contig14284_RC | Contig43791_RC | V00522 | Contig31771_RC | Contig40128_RC | Contig50950_RC | Contig34303_RC | NM_002104 | NM_002121 | NM_002122 | NM_002124 | NM_002125 | NM_002127 | NM_002135 | Contig49079_RC | NM_002145 | NM_002147 | U10991 | NM_002160 | NM_001432 | NM_002164 | NM_001438 | NM_001444 | NM_001446 | NM_001448 | NM_001450 | NM_001453 | NM_000727 | NM_000734 | NM_000735 | NM_002193 | NM_002196 | M24895 | NM_019598 | NM_000764 | NM_000766 | NM_000767 | Contig57584_RC | NM_000779 | NM_000783 | NM_000799 | Contig34989_RC | Contig41813_RC | Contig7558_RC | Contig42518_RC | AL157488 | Contig17248_RC | AL157492 | Contig51151_RC | Contig58301_RC | NM_002201 | Contig63748_RC | NM_002216 | NM_001504 | NM_001505 | NM_001511 | NM_002245 | NM_018901 | NM_002250 | NM_001523 | NM_018910 | NM_000802 | NM_002266 | NM_001540 | NM_002274 | NM_001546 | NM_002275 | NM_002276 | NM_001548 | Contig51486_RC | NM_000824 | NM_001554 | NM_000826 | NM_001555 | NM_018942 | NM_001565 | NM_002299 | NM_018950 | NM_018952 | Contig55606_RC | D25217 | NM_000846 | NM_000849 | Contig29022_RC | Contig36312_RC | Contig38980_RC | NM_000853 | NM_000854 | NM_000860 | Contig29014_RC | Contig46616_RC | NM_000888 | NM_000898 | AF067420 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 43 | 0 | 14.817248 | 14.817248 | 0 | 0 | 1 | 1 | 25 | 0 | 2 | 3 | 1 | -0.413955 | -0.954246 | 0.051024 | -0.111203 | -0.050066 | -0.340897 | -0.038770 | -0.104256 | -0.485671 | -0.565482 | 0.159871 | 0.198546 | 0.131750 | 0.600214 | 0.034498 | 0.492915 | 0.032284 | 0.020498 | 0.162939 | -0.026182 | 0.090109 | -0.110716 | 0.025322 | 0.690928 | 0.764825 | 0.579042 | 0.039256 | -0.112008 | -0.014334 | 0.012805 | 0.746400 | -0.020099 | -0.070284 | -0.410170 | 0.139930 | 0.313297 | 0.645509 | -0.122269 | -0.082007 | 0.351892 | 0.208250 | 0.064086 | -0.436356 | -0.061120 | -0.218654 | -0.063444 | -0.458989 | -0.042714 | 0.107555 | -0.046282 | -0.607396 | -0.298014 | -0.020523 | -0.380730 | -0.731905 | -0.208567 | -0.009658 | 0.264321 | -0.468796 | -0.043941 | -0.469030 | 0.215989 | -0.386191 | -0.412871 | 0.059138 | -0.188744 | -0.338177 | 0.293378 | -0.206389 | -0.662552 | -0.393674 | -0.379861 | -0.052162 | -0.602635 | -0.009926 | 0.084592 | 0.034111 | -0.168077 | 0.386483 | 0.564885 | 0.567547 | -0.082433 | -0.326897 | 0.040499 | -0.459177 | 0.000084 | -0.009372 | -0.429403 | -0.009102 | -0.249661 | -0.028993 | -0.361512 | -0.407895 | -1.222812 | -0.015455 | -0.423545 | 0.112265 | -0.047353 | 0.082021 | -0.602528 | -0.190188 | -0.145215 | 0.004135 | -0.176333 | -0.110213 | -0.033205 | 0.155177 | 0.323255 | -0.073127 | -0.023413 | -0.311654 | 0.009236 | -0.044002 | 0.132608 | -0.334047 | -0.808405 | -0.323191 | -0.577493 | 0.360844 | 0.111842 | -0.697586 | 0.735811 | 0.051385 | -0.033327 | -0.459596 | -0.286968 | -0.360244 | -0.249860 | -0.415610 | -0.509504 | 0.007801 | 0.318176 | -0.447874 | 0.175315 | 0.139464 | 0.032280 | 0.017776 | -0.159254 | 0.089921 | -0.295980 | -0.026401 | -0.726821 | -0.046697 | -0.550422 | -0.022145 | -0.193019 | -0.519450 | -0.402447 | 0.064943 | 0.039777 | -0.156050 | -0.046716 | 0.228131 | -0.363149 | 0.055404 | -0.411615 | -0.008090 | -0.049442 | -0.482440 | 0.001560 | 0.174193 | 0.107997 | -0.047147 | 0.020358 | 0.230408 | -0.044822 | -0.021826 | 0.039778 | -0.023779 | -0.099338 | 0.027804 | -0.334311 | -0.290268 | 0.288159 | -0.268857 | 0.038376 | 0.078023 | -0.206487 | 0.203593 | 0.022346 | -0.158805 | -0.000548 | -0.026872 | -0.063913 | -1.255312 | -0.776470 | -0.433047 | -0.427792 | 0.690093 | 0.346274 | -0.221116 | -0.295485 | 0.065812 | -0.350631 | 0.034709 | 0.029649 | -0.165704 | -0.339591 | -0.488278 | -0.598806 | -0.298633 | -0.002054 | 0.015005 | -1.038180 | -0.127618 | -0.808247 | -0.125292 | -0.018260 | -0.241934 | 0.216943 | -0.288201 | 0.313421 | -0.063127 | 0.103776 | 0.248185 | -0.002581 | 0.001084 | -0.158336 | -0.058907 | -0.582055 | 0.063776 | -0.198162 | -0.082942 | -0.051664 | -0.153165 | -0.020687 | 0.139511 | -1.145294 | 0.035330 | -0.302336 | 0.044315 | -0.033731 | -0.005392 | 0.040582 | -0.327379 | -0.268824 | -0.376223 | -0.297097 | -0.040401 | 0.000271 | 0.446467 | 0.054536 | -0.362549 | -0.031451 | 0.078009 | -0.093922 | 0.050757 | 0.063858 | -0.041906 | 0.084627 | -0.013284 | 0.042910 | -0.175794 | -0.099577 | 0.099069 | -0.450512 | -0.247531 | 0.307664 | -0.027422 | 0.090137 | 0.090124 | 0.124330 | 0.168662 | -0.712060 | 0.116849 | -0.070864 | -0.211524 | -0.780629 | -0.018973 | 0.047492 | -0.099444 | -0.115594 | -0.018486 | 0.080852 | -0.029850 | -0.437499 | -0.012642 | -0.404583 | 0.047153 | -0.259754 | -0.387366 | -0.059239 | 0.349518 | 0.005440 | 0.152618 | -0.234495 | -0.251840 | -0.201667 | 0.018184 | -0.034924 | -0.042962 | -0.241598 | -0.135843 | 0.014948 | 0.103380 | 0.125368 | -0.237190 | -0.202157 | -0.324501 | 0.340975 | -0.022907 | -0.025128 | -0.173104 | -0.028960 | 0.034702 | 0.666339 | -0.034403 | -0.017543 | 0.035296 | 0.036478 | 0.062420 | -0.072516 | -0.230958 | 0.025360 | 0.050819 | 0.019123 | -0.103237 | 0.028916 | 0.850946 | -0.018488 | -0.349869 | 0.378272 | 0.032493 | 0.007335 | 0.022278 | 0.023465 | -0.115366 | 0.105575 | 0.508975 | -0.485709 | 0.293928 | -0.056350 | 0.015563 | 0.089238 | -0.145029 | -0.033357 | 0.244765 | -0.141785 | 0.455231 | -0.026022 | 0.288433 | 0.000762 | -0.009070 | 0.060063 | -0.018272 | -0.159265 | -0.165408 | 0.036770 | -0.393202 | 0.188466 | -0.390145 | -0.078948 | -0.230609 | 0.087862 | 0.000913 | -0.072628 | -0.027639 | -0.030199 | -0.182992 | 0.031198 | 0.061926 | -0.265381 | -0.035741 | -0.031254 | 0.029012 | -0.003052 | 0.026914 | -0.043320 | -0.026653 | 0.099213 | -0.024610 | -0.051887 | 0.274215 | -0.009585 | 0.007964 | -0.004039 | 0.067666 | -0.302559 | 0.142586 | -0.420908 | -0.031337 | -0.037826 | -0.493759 | 0.053945 | 0.247277 | -0.144270 | -0.645865 | -0.291961 | -0.208671 | 0.157529 | -0.258301 | 0.049330 | -0.071196 | -0.031812 | 0.072738 | -0.151311 | -0.376297 | 0.047425 | -0.631842 | -0.341398 | 0.026805 | 1.029491 | -0.572065 | -0.823665 | 0.348644 | -0.019809 | -0.004109 | -0.255947 | 0.107973 | -0.001952 | -0.406550 | -0.013596 | 0.005508 | -0.244396 | 0.000586 | -0.141949 | 0.086238 | -0.124468 | -0.047825 | -0.080949 | -0.326594 | 0.045278 | 0.064318 | 0.078664 | -0.229389 | -0.005555 | 0.024477 | -0.099741 | -0.003050 | -0.289502 | -0.592376 | -0.047231 | 0.272070 | 0.063480 | 0.396041 | 0.194299 | -0.227419 | -0.122415 | 0.134577 | 0.104145 | -0.261220 | 0.255612 | -0.090416 | -0.024945 | 0.090673 | 0.147574 | 0.346098 | -0.035984 | -0.216902 | -0.122421 | -0.172396 | 0.065702 | -0.123643 | 0.016981 | 0.474905 | -0.503899 | 0.088386 | -0.077658 | -0.125006 | 0.000525 | 0.077414 | 0.012211 | -0.322484 | 0.003903 | -0.109496 | 0.069052 | -0.315251 | -0.066742 | 0.006203 | -0.426635 | -0.176968 | -0.192923 | 0.500912 | -0.000697 | 0.208796 | -0.117026 | -0.282315 | -0.403095 | -0.003093 | 0.202055 | -0.075199 | -0.241082 | -0.250433 | 0.174994 | -0.305636 | 0.252559 | 0.102568 | -0.027595 | -0.082577 | -0.127959 | 0.088424 | -0.228622 | 0.148421 | -0.010307 | 0.038442 | -0.178294 | 0.263184 | 0.150763 | -0.043846 | 0.107667 | 0.109201 | 0.083272 | -0.496534 | -0.019407 | -0.616885 | -1.053576 | -0.915034 | 0.021224 | -0.215953 | -0.256052 | -0.224341 | -0.304609 | -0.285498 | -0.316889 | -0.085097 | -0.137409 | -0.810887 | -0.018999 | -0.022114 | -0.207246 | 0.278200 | 0.400262 | 0.061543 | -0.016341 | -0.195359 | 0.063818 | 0.072518 | 0.029874 | 0.156765 | -0.039239 | -0.235931 | -0.121981 | -0.231424 | -0.011796 | -0.133221 | -0.686009 | -0.243122 | 0.585089 | -0.429946 | 0.080174 | -0.078348 | -0.525515 | -0.362415 | -0.357739 | 0.038928 | -0.098361 | -0.287254 | 0.986683 | -0.342991 | -0.399946 | 0.065438 | 0.102574 | 0.336812 | -0.049632 | 0.000982 | -0.090121 | -0.401016 | 0.940329 | 0.114049 | -0.000739 | 0.724788 | -0.240739 | -0.173595 | 0.004418 | -0.220937 | -0.159646 | 0.144037 | -0.093865 | 0.457166 | -0.389063 | 0.019155 | -0.375018 | -0.165263 | -0.239462 | 0.194095 | -0.345675 | 0.076786 | -0.293585 | -0.102607 | -0.110351 | 0.034771 | -0.447072 | -0.060994 | -0.046053 | -0.243348 | -0.031618 | 0.187668 | 0.073328 | -0.007447 | -0.004134 | -0.033715 | 0.014912 | -0.092893 | -0.078546 | -0.570467 | -0.137248 | 0.222989 | -0.136725 | 0.122359 | 0.012070 | -0.364229 | 0.291202 | -0.485465 | -0.180461 | 0.508701 | 0.338996 | 0.230191 | 0.323891 | 0.463534 | 0.013906 | -0.154915 | -0.202486 | -0.056503 | -0.120121 | -0.002159 | -0.456241 | -0.228017 | -0.363743 | -0.090892 | 0.064828 | 0.092622 | 0.320865 | -0.302970 | -0.035402 | -0.441947 | 0.454057 | -0.293202 | -0.106359 | 0.033490 | -0.152384 | 0.633331 | -0.053066 | -0.143191 | -0.020424 | -0.334249 | 0.055465 | -0.089875 | -0.359964 | -0.215243 | 0.157190 | -0.433985 | -0.309948 | 0.010978 | -0.224942 | 0.357958 | -0.582258 | -0.415599 | 0.119365 | 0.044797 | -0.006078 | -0.064726 | 0.267502 | 0.097199 | -0.374955 | -0.385663 | -0.056135 | -0.175061 | -0.414008 | -0.263490 | -0.028626 | -0.157850 | -0.156005 | -0.343186 | -0.228696 | 0.008815 | -0.110559 | 0.014129 | -0.015148 | -0.060394 | -0.466302 | -0.516789 | 0.094672 | -0.067449 | -0.684423 | 0.019766 | -0.098865 | -0.028949 | -0.307980 | 0.353250 | -0.683416 | -0.151175 | -0.219048 | 0.095451 | 0.069298 | -0.081564 | -0.164518 | -0.488889 | -0.047569 | 0.000384 | -0.373145 | 0.079118 | -0.741131 | -0.502387 | -0.314646 | -0.397837 | -0.311240 | 0.560099 | -0.119073 | -0.409840 | -0.089085 | 0.014876 | 0.215229 | 0.043629 | -0.163990 | -0.018848 | -0.005678 | -0.388945 | -0.000580 | -0.573688 | 0.138612 | 0.280107 | -0.527695 | -0.100785 | 0.337521 | 0.066960 | -0.761916 | 0.343763 | -0.014596 | 0.014792 | 0.221218 | 0.151054 | -0.137239 | 0.721447 | 0.146616 | -0.380067 | -0.355565 | -0.011620 | 0.136789 | 0.213545 | -0.700295 | -0.323366 | -0.176993 | 0.208135 | -0.074068 | -0.578048 | -0.328526 | 0.049274 | 0.021320 | -0.025902 | -0.344651 | -0.050634 | -0.878194 | 0.037790 | -0.380567 | -0.019843 | 0.312401 | -0.318086 | -0.095680 | -0.027661 | -0.292490 | 0.157155 | 0.208360 | -0.379841 | 0.176678 | -0.355846 | -0.373579 | -0.352349 | 0.059146 | 0.160620 | -0.097351 | -0.030787 | -0.567258 | -0.397905 | -0.301581 | -0.126906 | 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0.399830 | 0.118856 | 0.096947 | -0.055315 | -0.003603 | 0.034884 | -0.170949 | 0.172618 | -0.121197 | 0.075295 | 0.009753 | 0.457573 | -0.051543 | 0.541366 | 0.252780 | 0.040436 | -0.116955 | -0.093377 | 0.241544 | 0.894444 | -0.171993 | -0.549880 | -0.106865 | -0.300265 | -0.345751 | 0.089603 | -0.098789 | -0.010436 | -0.735016 | -0.232037 | 0.010197 | -0.506938 | 0.111344 | 0.005607 | 0.397777 | -0.712457 | -0.128258 | 0.094037 | -0.414341 | -0.151415 | 0.322722 | -0.420024 | 0.276973 | -0.200812 | 1.104388 | 0.093635 | -0.176200 | 0.208906 | 0.025874 | -0.003339 | 0.147028 | 0.047414 | -0.449703 | -0.052471 | -0.221507 | -0.293736 | -0.015674 | -0.089781 | -0.112932 | -0.003450 | 0.741507 | 0.022977 | -0.284088 | 0.032787 | 0.003422 | -0.454931 | -0.698241 | -0.162360 | -0.384384 | -0.212853 | -0.406002 | -0.154009 | -0.294200 | -0.160998 | -0.180928 | -0.419566 | 0.203176 | -0.300922 | 0.028845 | -0.441735 | -0.190916 | -0.381814 | 0.864119 | -0.206002 | -0.991899 | -0.302602 | 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| -0.192224 | -0.225232 | 0.010610 | 0.050936 | -0.305073 | -0.147573 | -0.178554 | -0.281964 | -0.325955 | -0.269678 | -0.490582 | -0.250304 | -0.355266 | 0.161447 | -0.571985 | -0.149774 | 0.127646 | 0.025476 | -0.120067 | -0.061446 | -0.125465 | -0.205151 | -0.013766 | 0.003618 | -0.320453 | -0.000969 | -0.532443 | 0.204407 | -0.171421 | 0.179370 | 0.031894 | 0.027669 | -0.012206 | -0.283950 | -0.022390 | -1.093536 | -0.521462 | -0.004539 | 0.146753 | -0.094156 | -0.606398 | -0.456657 | -0.225634 | -0.412652 | -0.218738 | -0.310150 | 0.036868 | -1.103862 | -0.339426 | -0.183171 | -0.107933 | -0.307549 | 0.013881 | -0.317378 | -0.355289 | 0.047610 | -0.172340 | -0.018605 | 0.368593 | 0.131647 | -0.220365 | -0.071047 | -0.413763 | 0.329271 | -0.177341 | 0.435084 | -0.029676 | -0.455784 | -0.172912 | -0.676051 | -0.031290 | 0.057281 | -0.546480 | -0.192392 | 0.010743 | -0.393641 | -0.355763 | -0.345857 | -0.144887 | -0.593635 | -1.090065 | -0.617900 | -0.025652 | -0.356793 | -0.479981 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| -0.090135 | 0.077232 | -0.106887 | 0.631550 | -0.782378 | 0.044710 | 0.025759 | 0.272959 | -0.601840 | -0.211200 | -0.433870 | 0.138867 | 0.286057 | 0.007087 | 0.482471 | -0.330577 | -0.526777 | 0.503809 | -0.123879 | 0.111167 | -0.095413 | -0.141392 | -0.028161 | -0.703285 | 0.281897 | -0.182338 | 0.384441 | 0.540899 | -0.304475 | -0.104981 | -0.541951 | -0.591898 | -0.107908 | -0.337010 | -1.002834 | 0.396417 | 0.035160 | -0.364242 | -0.156396 | -0.212224 | -0.612226 | -0.137140 | -0.017236 | 0.185274 | 0.387456 | -1.234464 | -0.618305 | -0.760009 | -0.401705 | -0.058961 | -0.099680 | -0.059907 | -0.173686 | -0.005235 | 0.346016 | -0.139232 | -0.293573 | 0.004920 | 0.149639 | -0.131834 | 0.491023 | -0.326954 | 0.242577 | -0.440495 | -0.079654 | -0.129174 | -0.440579 | 0.091784 | -0.385110 | -0.289255 | -0.485699 | -0.405309 | -0.109878 | -0.472279 | -0.331498 | -0.358591 | -0.070151 | -0.145741 | -0.247676 | -0.551571 | -0.201436 | -0.323253 | -0.031295 | -0.596451 | -0.891091 | 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| 1 | 48 | 0 | 14.261465 | 14.261465 | 0 | 0 | 0 | 1 | 20 | 0 | 3 | 3 | 1 | 0.195251 | 0.244626 | -0.199602 | -0.111397 | -0.135207 | 0.026813 | -0.165675 | 0.081549 | -0.736914 | 0.118127 | -0.183824 | 0.265609 | -0.080799 | -0.034788 | 0.146600 | 0.096864 | 0.043611 | 0.049600 | 0.150307 | -0.150096 | -0.082436 | -0.117535 | -0.245531 | -0.070265 | 0.046395 | 0.055705 | 0.088564 | -0.090754 | -0.200864 | -0.071210 | -0.418519 | -0.105610 | -0.072491 | 0.086513 | -0.014931 | -0.060254 | 0.005230 | -0.151410 | -0.282336 | 0.023717 | 0.139540 | -0.109258 | -0.242563 | -0.046313 | -0.077276 | -0.101312 | -0.463026 | -0.080892 | -0.151257 | -0.095722 | -0.277591 | -0.534005 | -0.006091 | -0.426614 | -0.610194 | 0.214168 | -0.102804 | 0.231918 | -0.378103 | -0.496814 | -0.673271 | -0.181023 | 0.461000 | 1.033179 | -0.064528 | 0.105519 | -0.379051 | -0.202288 | 0.044582 | 0.212550 | 0.258632 | 0.248212 | -0.288658 | -0.330377 | -0.205875 | -0.374288 | -0.670042 | -0.258033 | -0.208707 | 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-0.183322 | -0.200229 | -0.432539 | -0.026365 | 0.167478 | 0.198976 | -0.028399 | 0.253994 | 0.012851 | -0.049010 | 0.443326 | -0.121387 | -0.426001 | -0.147592 | 0.073405 | -0.256728 | -0.109046 | -0.277766 | -0.030657 | -0.002193 | -0.173473 | -0.158494 | -0.332226 | -0.551058 | 0.085417 | -0.234049 | -0.177247 | -0.599641 | -0.295680 | 0.054397 | -0.594397 | 0.192997 | 0.061050 | 0.223206 | -0.202125 | -0.299003 | -0.074905 | -0.192290 | 0.016294 | 0.201630 | -0.123971 | 0.447121 | 0.255317 | -0.043335 | 0.230541 | -0.119930 | 0.354213 | 0.102082 | 0.220068 | 0.013762 | -0.009616 | -0.328582 | 0.173791 | 0.174843 | 0.145188 | 0.031569 | -0.111305 | -0.136801 | 0.070222 | 0.296437 | 0.412212 | -0.257070 | 0.183530 | 0.304189 | -0.046460 | -0.739003 | 0.652174 | -0.413822 | 0.024438 | -0.115231 | -0.023845 | -0.000144 | 0.309551 | -0.212157 | -0.525938 | 0.173627 | -0.586561 | 0.044907 | 0.079308 | -0.112321 | -0.036256 | 0.287903 | -0.128028 | 0.010729 | -0.155962 | -0.094049 | 0.015181 | -0.000769 | -0.019806 | -0.061202 | -0.170884 | -0.176239 | 0.056039 | 0.008862 | -0.180236 | 0.340403 | -0.019238 | -0.033850 | 0.285858 | -0.290392 | 0.261976 | -0.099940 | -0.055403 | -0.260567 | -0.142633 | 0.558457 | 0.056172 | -0.287936 | 0.370841 | -0.270661 | -0.598039 | -0.286576 | -0.322454 | -0.319299 | -0.333672 | 0.637767 | -0.146010 | 0.197740 | 0.269588 | -0.058550 | 0.185193 | 0.142597 | 0.285717 | 0.333231 | -0.558496 | 0.443758 | 0.079911 | 0.118197 | 0.538226 | -0.089828 | -0.117279 | -0.040890 | -0.561073 | 0.201186 | -0.171836 | -0.222117 | -0.206442 | 0.048060 | 0.059469 | 0.047308 | 0.233957 | -0.116470 | 0.188007 | 0.110399 | -0.385205 | 0.091373 | -0.168974 | 0.089601 | -0.317865 | -0.344896 | -0.285015 | -0.182390 | -0.241431 | -0.298021 | -0.567537 | -0.128182 | 0.006284 | 0.331118 | -0.301391 | -1.071904 | -0.132179 | -0.315731 | -0.200676 | 0.002598 | -0.446901 | -0.087623 | -0.032645 | -0.031120 | 0.100233 | -0.318736 | 0.093453 | -0.235396 | -0.392244 | -0.413503 | -0.446512 | -0.063237 | 0.075500 | 0.036606 | -0.055871 | -0.420466 | 0.349044 | -0.228439 | 0.223573 | -0.154759 | -0.366572 | -0.157722 | 0.306653 | 0.188982 | -0.257875 | -0.092154 | 0.047555 | -0.697922 | 0.380124 | 0.091886 | -0.329443 | 0.058778 | 0.377745 | -0.291611 | 0.601145 | -0.140849 | -0.558299 | -0.243845 | -0.241327 | -0.010851 | 0.087439 | -0.006346 | -0.073454 | -0.194642 | -0.236129 | 0.115404 | -0.306316 | 0.169532 | 0.104859 | 0.160285 | -0.177451 | -0.221596 | -0.205492 | 0.169171 | 0.009662 | -0.300705 | 0.596177 | -0.069947 | -0.101185 | 0.160966 | -0.184200 | 0.092120 | 0.448235 | 0.044272 | -0.165467 | -0.390687 | 0.296300 | -0.272776 | 0.291347 | -0.253667 | 0.067141 | -0.136360 | 0.125658 | -0.488291 | 0.458227 | -0.103081 | 0.074245 | -0.293499 | -0.435191 | 0.400258 | -0.290411 | -0.311525 | -0.183303 | -0.507848 | -0.016329 | 0.146441 | 0.258095 | 0.018138 | -0.013028 | 0.060670 | 0.211035 | -0.242323 | 0.060509 | 0.238882 | 0.076257 | -0.247387 | 0.070201 | 0.118146 | -0.149749 | -0.215155 | -0.113520 | -0.237709 | -0.503718 | 0.189851 | -0.665466 | -0.351137 | 0.196119 | 0.409588 | -0.376895 | -0.041689 | 0.063841 | -0.251184 | -0.202518 | 0.588629 | -0.227035 | -0.970734 | -0.091323 | -0.362425 | 0.050314 | -0.132012 | 0.175395 | 0.155090 | -0.333560 | -0.171254 | -0.141561 | -0.350619 | -0.651954 | -0.239312 | -1.195136 | -0.667773 | 0.442996 | 0.080440 | -0.101346 | -0.564406 | 0.258803 | -0.417140 | -1.145413 | 0.485099 | -0.011982 | 0.117087 | -0.687755 | -0.100395 | -0.110103 | -0.010583 | -0.480546 | -0.125940 | -0.233138 | -0.410724 | 0.170439 | -0.105234 | -0.392757 | 0.082374 | -0.046546 | -0.452707 | 0.201419 | -0.934033 | 0.541752 | -0.223858 | -0.066753 | -0.009448 | -0.048519 | -0.228261 | -0.717238 | 0.219708 | -0.075041 | -0.582351 | -0.352675 | 0.006722 | -0.291800 | -0.271591 | -0.336880 | 0.022188 | -0.480788 | -0.687938 | -0.374235 | 0.022296 | -0.287806 | -0.427857 | 0.404436 | -0.026787 | 0.532303 | 0.069036 | -0.138987 | -0.072021 | 0.095341 | -0.243872 | 0.069593 | -0.491548 | 0.302262 | 0.214189 | -0.153346 | 0.392273 | 0.077507 | 0.362663 | -0.064416 | -0.296285 | 0.229334 | -0.976162 | 0.145884 | 0.001760 | 0.188669 | 0.162272 | 0.165372 | 0.415875 | 0.541351 | -0.532393 | 0.315586 | -0.002711 | -0.171778 | -0.144736 | -0.011610 | -0.440074 | -0.135573 | 0.287677 | 0.100407 | -0.673922 | -0.408616 | 0.311632 | -0.314584 | 0.000325 | -0.240810 | -0.221086 | -0.581057 | -0.536492 | -0.103906 | 0.169255 | -0.035665 | -0.189014 | -0.119743 | -0.243585 | -0.095068 | 0.481303 | 0.131189 | 0.149247 | -0.192614 | 0.038559 | -0.165305 | 0.658240 | -0.322834 | -0.157391 | 0.139885 | -0.035913 | 0.324696 | 0.306426 | -0.417845 | -0.465885 | -0.468013 | -0.292309 | 0.340959 | 0.452132 | -0.482058 | -0.236302 | 0.285367 | -0.339917 | 0.171340 | 0.391128 | 0.307351 | 0.198469 | -0.305602 | -0.266555 | -0.274115 | -0.318845 | -0.301571 | 0.037930 | 0.130978 | 0.311680 | -0.124273 | 0.245384 | -0.508773 | -0.114573 | -0.430745 | 0.358304 | 0.170395 | -0.009106 | -0.558650 | 0.496150 | 0.283010 | -0.373419 | -0.141074 | -0.444533 | 0.052032 | 0.526278 | -0.287922 | -0.024397 | -0.126292 | 0.250293 | -0.379591 | 0.176434 | -0.537141 | -0.338271 | 0.111791 | -0.165214 | -0.131572 | 0.025060 | -0.456114 | -0.494807 | 0.275279 | -0.319763 | -0.952354 | 0.272332 | -0.052670 | -0.393308 | -0.046095 | -0.319538 | -0.427367 | -0.127232 | -0.214976 | -0.201319 | -0.304842 | -1.146720 | -0.517358 | 0.273744 | -0.095488 | 0.126990 | -0.338886 | -0.245286 | 0.229988 | -0.084045 | -0.171319 | -0.333938 | 0.100245 | -0.557075 | 0.510980 | -0.171551 | 0.070004 | -0.075787 | 0.043876 | -0.196606 | -0.220376 | 0.179152 | -0.470602 | -0.080452 | 0.611942 | -0.018856 | 0.170203 | 0.609129 | -0.080603 | -0.255806 | -0.233129 | 0.251981 | 0.583411 | 0.827428 | -1.056209 | -0.068477 | 0.011319 | -0.037401 | -0.479714 | 0.774578 | -0.256280 | -0.010993 | -0.445192 | -0.191234 | -0.088751 | -0.200597 | 0.355198 | -0.363763 | -0.364457 | 0.211452 | 0.519197 | 0.328736 | -0.047571 | 0.084228 | -0.695950 | -0.402840 | -0.099965 | 0.110155 | -0.114298 | 0.258495 | -0.198911 |
| 3 | 50 | 0 | 7.748118 | 7.748118 | 0 | 1 | 0 | 1 | 15 | 1 | 2 | 3 | 1 | 0.501286 | -1.071614 | -0.206041 | -0.051775 | -0.049676 | -0.306176 | 0.061596 | 0.115982 | -0.842997 | -0.020820 | -0.189779 | -0.003238 | 0.450045 | -0.024921 | -0.187729 | 0.012188 | -0.110403 | -0.036232 | -0.014174 | -0.192298 | -0.116583 | -0.117127 | -0.082694 | -0.005107 | -0.074346 | 0.090662 | 0.214941 | -0.114225 | -0.112160 | -0.108567 | -0.192184 | -0.090999 | -0.093462 | -0.587146 | 0.095157 | 0.029532 | 0.038152 | -0.055742 | -0.056952 | -0.234063 | -0.045315 | 0.014587 | -0.312572 | -0.106923 | -0.589406 | -0.041847 | -0.521765 | -0.091159 | 0.174891 | -0.110732 | 0.081555 | -0.388621 | -0.067356 | 0.638883 | -0.752105 | -0.238773 | -0.046779 | 0.296058 | -0.495123 | -0.060168 | 0.147430 | -0.135722 | -0.560134 | -0.313288 | -0.058693 | -0.056777 | 0.423212 | -0.282164 | 0.179245 | -0.740274 | -0.879258 | -0.858772 | -0.064678 | 0.267690 | -0.269906 | 0.022762 | 0.019408 | -0.150287 | -0.666152 | 0.178200 | 0.118723 | 0.023592 | -0.522390 | -0.060704 | -0.365705 | -0.080313 | -0.178891 | -0.049695 | 0.003398 | -0.312674 | -0.042298 | -0.268126 | -0.160847 | -0.682960 | -0.054801 | -0.227748 | 0.077870 | -0.020769 | 0.109470 | -0.656663 | -0.135591 | -0.234001 | -0.060119 | 0.278747 | 0.009014 | 0.122116 | -0.040892 | 0.307104 | 0.210041 | -0.080372 | -0.310906 | -0.134424 | -0.048080 | -0.389279 | -0.295675 | -0.645735 | 0.984978 | 0.689372 | 0.974152 | -0.399570 | -0.674144 | 0.195040 | -0.147739 | -0.054888 | -0.395504 | -0.017605 | -0.297058 | -0.296589 | -0.573276 | -0.551571 | 0.226803 | 0.364035 | 0.470708 | 0.087861 | -0.178204 | -0.257769 | -0.036250 | -0.156012 | 0.215458 | -0.268712 | -0.007521 | -0.544872 | -0.466819 | -0.128685 | -0.124449 | 0.067411 | 0.558743 | -0.178397 | -0.008702 | -0.032943 | 0.013507 | -0.697316 | 0.254120 | -0.448597 | 0.031670 | -0.405181 | -0.093909 | -0.091210 | -1.149129 | -0.006924 | -0.419583 | -0.101840 | -0.424539 | -0.016541 | -0.017839 | 0.077636 | -0.014860 | -0.494307 | 0.007683 | -0.006810 | 0.070349 | 0.389110 | -0.088048 | 0.132888 | -0.489917 | -0.050950 | 0.027699 | -0.211570 | 0.025721 | -0.204386 | 0.322514 | 0.049346 | 0.009691 | 0.026978 | -0.204086 | 0.151625 | -0.189351 | -0.410359 | -0.182628 | -0.061359 | -0.198978 | -0.361924 | -0.385932 | 0.227887 | 0.031285 | 0.000231 | 0.576344 | -0.427218 | -0.390091 | -0.662897 | -0.116665 | -0.033925 | -0.031083 | -0.113333 | -0.065483 | -0.517197 | -0.282468 | -0.078513 | 0.013155 | 0.212350 | -0.217888 | -0.316237 | 0.289619 | -0.041822 | -0.197730 | -0.072982 | -0.062612 | -0.210749 | -0.008081 | -0.266755 | -0.596916 | -0.175491 | 0.175986 | -0.085860 | -0.741099 | -0.038436 | 0.565607 | 0.615656 | 0.016522 | -0.364043 | -0.004020 | -0.493007 | 0.028144 | 0.074426 | -0.169952 | -0.342389 | -0.292908 | -0.556548 | 0.064896 | -0.076833 | -0.200393 | -0.008412 | -0.322337 | -0.015375 | -0.025249 | -0.120858 | -0.064012 | -0.024589 | -0.058083 | -0.091206 | 0.266065 | 0.124197 | 0.538679 | -0.153330 | 0.010334 | 0.075751 | 0.401266 | 0.217241 | -0.115728 | -0.006042 | -0.053746 | 0.005484 | 0.061824 | -0.686313 | -0.265317 | -0.044101 | -0.315045 | -0.391535 | -0.024019 | 0.029691 | -0.195280 | 0.087381 | -0.358761 | -0.000188 | -0.037834 | -0.294186 | -0.285325 | -0.430169 | 0.136999 | -0.126958 | 0.572389 | -0.212871 | 0.458720 | 0.038372 | 0.222719 | -0.212264 | 0.231150 | -0.075585 | 0.553865 | -0.364569 | -0.063860 | -0.225302 | -0.357826 | -0.146976 | -0.047034 | -0.561763 | -0.288647 | -0.435037 | -0.465616 | 0.184856 | 0.299984 | -0.057756 | 0.267724 | -0.075529 | -0.070136 | -0.109972 | -0.180601 | -0.052610 | 0.017468 | -0.015843 | -0.276955 | -0.164750 | 0.519682 | 0.112095 | -0.045962 | -0.028554 | -0.230589 | -0.031939 | 0.244868 | -0.052564 | -0.278583 | -0.099920 | 0.121675 | 0.076392 | 0.252610 | 0.012829 | -0.188583 | 0.061375 | -0.319749 | -0.738669 | 0.433784 | -0.023382 | 0.042635 | -0.107498 | -0.211909 | -0.031121 | 0.648080 | -0.232687 | 0.072980 | -0.060321 | 0.116044 | -0.036372 | -0.454036 | -0.029950 | -0.195431 | 0.280342 | 0.240285 | -0.017111 | -0.370046 | -0.336276 | -0.367922 | -0.144996 | -0.151444 | 0.043237 | 0.140858 | -0.053587 | -0.030297 | -0.075839 | -0.100157 | -0.056624 | 0.094099 | -0.484501 | -0.267142 | -0.052065 | -0.051775 | -0.013969 | 0.045635 | -0.025725 | 0.003096 | 0.000451 | -0.031076 | 0.298061 | -0.188451 | -0.084388 | 0.024716 | -0.132586 | 0.006871 | -0.473599 | 0.037176 | -0.449718 | -0.125759 | -0.092525 | -0.372296 | 0.071270 | -0.107622 | 0.055034 | 0.069140 | -0.459160 | -0.150726 | -0.196481 | -0.011330 | -0.333452 | -0.301006 | -0.088663 | 0.029865 | -0.280835 | -0.328648 | 0.131150 | -0.638458 | -0.347144 | -0.048084 | 0.209742 | 0.706588 | -0.561098 | -0.661874 | -0.015130 | -0.000368 | -0.276742 | -0.076602 | -0.171878 | 0.133369 | -0.063718 | -0.017353 | -0.271033 | 0.139721 | -0.400567 | 0.009636 | -0.080039 | -0.068184 | 0.016549 | -0.478096 | 0.022359 | 0.081009 | 0.025212 | 0.403973 | 0.047318 | -0.072588 | 0.350694 | -0.135043 | 0.509815 | -0.190662 | 0.020777 | -0.129430 | 0.052072 | -0.137032 | -0.161318 | -0.097296 | 0.121024 | -0.151038 | 0.661656 | -0.144808 | 0.033166 | -0.315237 | -0.122472 | -0.249806 | -0.145500 | -0.007498 | 0.255155 | 0.412227 | -0.007524 | -0.382309 | -0.232168 | -0.291524 | 0.009294 | 0.648026 | -0.581493 | 0.091297 | -0.282479 | -0.290368 | -0.599869 | 0.033426 | -0.007993 | 0.453410 | 0.043174 | -0.142339 | -0.223527 | 0.093212 | -0.060891 | -0.050615 | -0.372441 | 0.040898 | 0.151569 | -0.019889 | -0.040522 | -0.094958 | 0.245115 | -0.450618 | -0.313666 | 0.081950 | 0.236491 | 0.135094 | -0.284411 | -0.457688 | -0.314043 | -0.296450 | 0.175163 | -0.244195 | 0.077684 | -0.370584 | -0.584455 | -0.165170 | -0.315981 | 0.057639 | -0.060290 | 0.011659 | -0.242457 | -0.250356 | -0.167579 | -0.299142 | 0.171208 | 0.252844 | 0.095317 | -0.143837 | 0.034299 | -0.423381 | -0.898918 | -0.806994 | -0.061766 | -0.208362 | 0.241206 | -0.229502 | -0.189047 | 0.202579 | -0.024787 | -0.269985 | -0.283140 | -0.689884 | -0.273023 | -0.788906 | -0.380007 | 0.024575 | -0.009317 | -0.176603 | 0.026463 | -0.136053 | -0.049635 | -0.052238 | 0.008858 | 0.229115 | 0.018393 | -0.137049 | -0.110733 | -0.208892 | 0.076187 | 0.073159 | -0.588574 | -0.223728 | 0.078340 | 0.040388 | 0.062749 | -0.001997 | -0.524041 | -0.330520 | 0.233847 | 0.017650 | -0.034207 | -0.222475 | 0.252275 | -0.375631 | -0.218205 | -0.259945 | -0.723417 | 0.000116 | -0.067019 | -0.365954 | -0.113767 | -0.473546 | -0.041024 | 0.112448 | 0.152701 | -0.540307 | -0.335570 | -0.163894 | 0.491420 | -0.264510 | -0.244054 | 0.118618 | -0.222598 | -0.127758 | -0.461348 | -0.052571 | -0.476770 | -0.269526 | -0.179070 | 0.727101 | -0.392943 | 0.082646 | 0.147250 | 0.228618 | 0.063429 | 0.055313 | 0.109687 | -0.050202 | 0.008329 | -0.025229 | -0.030947 | -0.305543 | 0.058238 | -0.015900 | 0.004957 | -0.023990 | 0.011420 | 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-0.162344 | -0.567571 | -0.222024 | 0.015191 | -0.029245 | -0.252507 | 0.148547 | -0.243376 | -0.454843 | 0.115288 | 0.061850 | 0.048662 | 0.096129 | -0.012913 | -0.226230 | 0.759299 | -0.233129 | -0.319359 | -0.405085 | 0.209131 | -0.668121 | -0.144698 | -0.103629 | 0.266355 | -0.163167 | -0.112102 | 0.240776 | -0.761039 | 0.009990 | -0.047393 | 0.066877 | 0.058700 | -0.303529 | -0.448086 | -0.460201 | 0.088518 | -0.619593 | 0.107419 | 0.543759 | -0.599898 | -0.437193 | -0.640261 | -0.260273 | 0.533011 | -0.179445 | -0.289648 | 0.043434 | -0.437132 | 0.316878 | -0.179194 | -0.435892 | -0.042390 | 0.163665 | -0.298232 | -0.412189 | -0.403466 | -0.150860 | 0.123560 | 0.141488 | -0.291213 | -0.267096 | 0.259332 | 0.003615 | -0.298187 | 0.215197 | -0.033543 | 0.051046 | -0.184602 | -0.367704 | -0.005397 | 0.033931 | -0.309038 | -0.337795 | 0.303917 | -0.306820 | -0.530936 | -0.078349 | -0.250523 | -0.246177 | -0.176316 | -0.291056 | -0.176329 | -0.542278 | 0.094754 | 0.098540 | -0.005169 | 0.398285 | 0.370880 | -0.208100 | 0.136262 | 0.134339 | -0.405545 | 0.127404 | -0.062166 | -0.106881 | -0.140052 | 0.334650 | -0.187528 | -0.266596 | -0.342358 | 0.007733 | -0.392288 | -0.264082 | 0.149031 | -0.507641 | -0.166538 | 0.082506 | -0.226431 | -0.172932 | -0.413240 | -0.417192 | -0.338242 | -0.781721 | -0.043931 | -0.196031 | 0.036601 | 0.044586 | -0.393937 | 0.292994 | 0.060675 | -0.272710 | -0.389843 | 0.238217 | -0.579103 | -0.369236 | 0.397816 | -0.241689 | 0.278348 | 0.266472 | -0.317175 | -0.555958 | -0.328557 | 0.421411 | 0.308437 | -0.242026 | 0.129413 | -0.016067 | 0.068665 | 0.398833 | 0.295309 | -0.805088 | -0.486580 | -0.764450 | -0.219668 | 0.112781 | 0.068792 | -0.150033 | -0.218975 | -0.408914 | 0.116232 | -0.120747 | -0.040496 | 0.771230 | -0.132066 | 0.562408 | 0.081616 | -0.020180 | -0.153053 | -0.060910 | -0.021021 | 0.525344 | 0.006565 | -0.539387 | -0.250328 | 0.218585 | 0.587630 | -0.048342 | -0.039979 | -0.202263 | 0.017664 | -0.021335 | -0.766373 | -0.665605 | -0.253438 | -0.073434 | 0.212666 | -0.342400 | 1.289895 | -0.274779 | -0.170571 | 0.002805 | -0.029681 | 0.495360 | 0.360814 | 0.101671 | 0.746574 | -0.674505 | 0.372093 | 0.208659 | -0.120259 | 0.005019 | 0.033362 | 0.184532 | 0.116067 | 0.241531 | -0.296907 | -0.044497 | -0.091418 | 0.261501 | 0.013810 | 0.476429 | -0.297544 | -0.271894 | 0.687524 | -0.332959 | -0.017819 | -0.348586 | -0.153596 | -0.219761 | -0.219845 | -0.410560 | 0.000253 | -0.740247 | -0.035302 | -0.208993 | -0.424131 | -0.140641 | -0.034478 | -0.241523 | -0.458891 | -0.024648 | 0.091396 | -0.096148 | -0.660341 | 0.033233 | 0.051157 | 0.770212 | -0.161083 | 0.319215 | -0.824555 | -0.156316 | 0.031476 | -0.469643 | -0.269426 | -0.598515 | 0.180354 | -0.191409 | -0.088579 | -0.058647 | -0.198640 | 0.336797 | -0.171511 | 0.391698 | 0.137233 | -0.191821 | 0.261941 | -0.376366 | -0.083052 | -0.061667 | -0.073902 | 0.211922 | -0.149715 | 0.432908 | -0.240450 | -0.029766 | 0.704321 | 0.185420 | 0.035896 | -0.594399 | -0.390538 | -0.055017 | -0.069327 | 0.067315 | -0.293668 | 0.013583 | -0.011189 | -0.003785 | -0.388068 | -0.383219 | -0.279857 | 0.172187 | 0.511886 | 0.105105 | -0.064028 | -0.061252 | 0.269429 | -0.364109 | -0.466846 | 0.001449 | -0.197049 | -0.152397 | 0.135306 | -0.067105 | -0.336383 | -0.012866 | -0.153312 | -0.040909 | -0.411493 | 0.010032 | -0.196506 | 0.252561 | -0.408667 | -0.187263 | 0.270461 | -0.440761 | -0.277013 | -0.598323 | -0.319744 | 0.341274 | -0.225833 | 0.600666 | 0.081057 | -0.230837 | 0.266320 | 0.039786 | -0.243422 | 0.042926 | 0.001138 | 0.115460 | 0.169967 | 0.149701 | -0.167475 | 0.094298 | 0.021457 | 0.074290 | 0.054986 | 0.173883 | -0.172970 | 0.065486 | 0.349871 | -0.087764 | 0.040513 | -0.708102 | 0.062555 | -0.254047 | -0.535803 | -0.565829 | -0.042203 | -0.139105 | 0.829902 | 0.311264 | -0.342824 | 0.178689 | -0.576579 | 0.043649 | 0.360736 | 0.207455 | -0.481013 | 0.664327 | -0.017444 | -0.307791 | -0.279718 | -0.022051 | -0.058550 | -0.167609 | -0.188210 | 0.009777 | -0.225060 | -0.789562 | 0.297826 | 0.446325 | -0.290056 | 0.025150 | -0.051381 | -0.042731 | 0.328504 | 0.257570 | -0.130744 | 0.103508 | -0.062519 | -0.184604 | -0.024384 | -0.148934 | 0.376510 | 0.426521 | -0.589915 | -0.111562 | 0.154312 | 0.049307 | -0.031592 | -0.282642 | -0.408090 | 0.107534 | -0.007572 | -0.349258 | 0.122479 | 0.080278 | 0.285465 | -0.076998 | 0.254892 | 0.359589 | -0.347847 | -0.154110 | -0.100371 | -0.014679 | -0.285617 | -0.305117 | -0.207808 | 0.188518 | -0.523906 | 0.450108 | -0.644780 | -0.337320 | -0.272606 | -0.244512 | 0.027989 | -0.056515 | -0.316695 | -0.393962 | 0.060586 | 0.723258 | 0.207474 | 0.003933 | 0.246477 | -0.219163 | -0.592344 | -0.118294 | -0.656355 | -0.413779 | -0.291812 | -0.227423 | -0.208646 | -0.251752 | 0.065805 | 0.615505 | -0.110570 | -0.958368 | -1.014097 | -0.347264 | 0.126401 | 0.092070 | -0.306253 | -0.072819 | -0.101637 | -0.137299 | -0.352033 | -0.586497 | -0.092133 | 0.364343 | -0.556532 | -0.190164 | 0.632591 | 0.376941 | -0.122180 | 0.179094 | -0.472998 | -0.350218 | -0.065001 | -0.523203 | 0.177035 | -0.232051 | -0.351763 | 0.372685 | 0.294619 | 0.304134 | 0.385788 | -0.130969 | -0.118598 | 0.396194 | -0.422702 | 0.168069 | -0.456861 | 0.026502 | -0.379222 | -0.274048 | -0.111351 | -0.000892 | 0.032969 | -0.255549 | -0.009503 | -0.028993 | 0.443478 | -0.351794 | -0.106869 | -0.112005 | -0.134332 | 0.262681 | 0.113354 | -0.222899 | 0.466865 | -0.096403 | -0.573781 | -0.206264 | -0.128770 | -0.397329 | -0.341858 | -0.260542 | 0.501286 | -0.053145 | -0.000203 | 0.041793 | -0.346537 | -0.355117 | 0.836328 | -0.435763 | -0.339983 | -0.367596 | 0.211867 | 0.197233 | 0.097868 | -0.483530 | -0.282229 | -0.320171 | 0.034425 | -0.352719 | -0.448852 | 0.182881 | -0.047139 | -0.088145 | 0.087094 | -0.345050 | -0.262394 | -0.312494 | -0.438570 | 0.203308 | 0.264044 | 0.513375 | -0.048844 | -0.170714 | -0.826129 | -0.438445 | -0.380840 | -0.238719 | -0.199875 | 0.045789 | -0.376790 | -0.004796 | 0.151964 | -0.791283 | 0.219097 | 0.705959 | -0.237844 | -0.608099 | 0.309562 | 0.210059 | -0.074500 | -0.283222 | -0.398688 | 0.673900 | -0.219956 | -0.330755 | -0.181248 | -0.064047 | 0.437301 | 0.069988 | -0.140563 | -0.985127 | -0.171128 | -0.454867 | -0.362581 | -0.605006 | 0.686163 | -0.398132 | 0.111812 | 0.202895 | -0.047989 | 0.039940 | -0.324642 | 0.222651 | 0.976676 | 0.646417 | -0.066304 | -0.298989 | 0.283218 | -0.278492 | -0.085837 | 0.436510 | -0.930378 | -0.184032 | -0.250049 | 0.305833 | -1.034569 | -0.263313 | -0.130627 | 0.213556 | -0.216478 | -0.075931 | -0.508236 | -0.445702 | -0.261098 | -0.035503 | 0.044155 | 0.187053 | 0.038345 | -0.469632 | -0.523677 | 0.024807 | 0.237869 | -0.013406 | 0.166980 | -0.457842 | -0.221759 | 0.624799 | -0.297413 | -0.346948 | -0.099796 | -0.352802 | 0.183402 | -0.118067 | -0.375277 | -0.510824 | -0.926409 | 0.472189 | -0.426247 | -0.565565 | -0.790261 | -0.574813 | -0.249868 | -0.218987 | 0.124899 | -0.362940 | -0.508475 | 0.251044 | -0.512522 | -0.430332 | 0.126479 | -0.121188 | -0.391849 | 0.939467 | -0.099851 | 0.202849 | 0.229342 | 0.220997 | 0.066387 | -0.299795 | 0.069708 | -0.241424 | 0.206132 | -0.609349 | 0.583086 | -0.357693 | 0.075470 | 0.444215 | -0.044599 | 0.276154 | 0.191095 | -0.411469 | 0.229133 | 0.209759 | -0.145047 | 0.125645 | 0.290361 | -0.498997 | 0.170154 | 0.997816 | 0.114081 | -0.825842 | -0.371961 | -0.207110 | 0.180954 | 0.261499 | -0.166714 | -0.007753 | -0.371883 | -0.406531 | -0.082655 | 0.420281 | -0.365384 | -0.405449 | -0.065553 | 0.231366 | -0.331244 | -0.108176 | 0.063804 | -0.197364 | -0.186033 | 0.508493 | 0.158739 | 0.380810 | -0.261152 | 0.120787 | -0.287776 | -0.296366 | -0.275321 | 0.182183 | -0.540092 | -0.345987 | 0.016178 | -0.421229 | -0.645285 | 0.057428 | -0.276029 | 0.171411 | -0.054152 | -0.207344 | 0.596006 | 0.119313 | 0.230015 | -0.770832 | -0.289100 | -0.156712 | -0.153782 | -0.368969 | -0.343178 | 0.216438 | -0.280283 | 0.693556 | 0.049214 | 0.689486 | -0.777743 | 0.513689 | 0.900089 | 0.517531 | 0.141145 | -0.201229 | -0.137770 | 0.774448 | -0.413888 | 0.009898 | 0.114044 | -0.046686 | -0.590853 | -0.114399 | -0.029809 | 0.278349 | -0.193947 | 0.219785 | -0.006463 | -0.533436 | -0.028114 | -0.172015 | 0.115734 | 0.042083 | -0.282671 | -0.538557 | -0.355338 | -0.439597 | 0.067401 | -0.149562 | -0.759391 | 0.306680 | -0.511917 | -0.447543 | -0.314267 | -0.208884 | -0.465281 | -0.330805 | 0.436123 | 0.281188 | -0.202479 | -0.608917 | -0.457443 | 0.319661 | -0.475031 | -0.206113 | 0.052275 | -0.197183 | -0.335951 | -0.332761 | -0.294997 | -0.179620 | 0.224798 | -0.730459 | 0.431316 | -0.153838 | 0.096750 | 0.004083 | 0.357040 | -0.509627 | -0.298738 | -0.131830 | -0.423847 | -0.732862 | -0.437769 | -0.337763 | -0.230526 | -0.371917 | -0.148544 | -0.554392 | -0.436530 | -0.298767 | -0.292425 | -0.658711 | 0.188053 | -0.361884 | 0.263228 | 0.155554 | -0.136231 | -0.139935 | -0.646764 | -0.231747 | -0.461515 | -0.231232 | -0.180521 | 0.160247 | -0.400131 | -0.429229 | -0.382146 | 0.189681 | -0.115396 | 0.648861 | -0.039088 | 0.182182 | -0.524640 | 0.037320 | -0.167688 | -0.016790 | -0.285344 | -0.251188 | 0.862710 |
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-0.137351 | -0.179152 | -0.177149 | 0.292143 | -0.355872 | -0.037229 | 0.182605 | -0.284449 | 0.297987 | -0.060623 | 0.187481 | -0.332493 | -0.254909 | -0.208652 | -0.332901 | -0.183677 | -0.314455 | -0.304050 | -0.109691 | -0.064158 | 0.069415 | -0.131634 | -0.959086 | -0.243936 | -0.389919 | -0.191652 | -0.070410 | 0.087514 | -0.113861 | -0.153738 | -0.216271 | -0.007382 | -0.121880 | 0.334028 | -0.055107 | -0.624138 | -0.407426 | -0.237982 | -0.346208 | -0.105216 | -0.214206 | 0.112792 | -0.075172 | -1.028206 | 0.245364 | -0.245473 | 0.214512 | -0.855520 | 0.077345 | -0.105126 | -0.063632 | -0.144903 | -0.027538 | -0.242970 | -0.644114 | -0.191815 | -0.203108 | -0.055127 | -0.023043 | -0.095728 | -0.571115 | -0.138990 | 0.063471 | -0.160607 | -0.076807 | 0.003046 | 0.073720 | 0.032633 | -0.249347 | -0.558028 | -0.030826 | -0.075879 | 0.210467 | 0.450031 | -0.224131 | -0.413039 | -0.210153 | -0.389096 | 0.020477 | -0.210792 | -0.907695 | -0.232505 | -0.218650 | 0.247467 | -0.220384 | 0.272390 | -0.191751 | -0.052880 | -0.044877 | -0.306516 | 0.206842 | -0.024740 | 0.191708 | -0.217992 | 0.124888 | 0.209359 | 0.141275 | -0.069405 | 0.243276 | 0.063059 | 0.256771 | 0.075669 | -0.386573 | 0.414981 | 0.637607 | 0.126196 | -0.104650 | -0.225521 | 0.237376 | -0.017837 | 0.226684 | -0.026611 | -0.601001 | 0.170819 | 0.280338 | -0.091438 | 0.006922 | 0.040201 | 0.031125 | 0.004657 | 0.081995 | 0.166126 | -0.871982 | -0.007858 | -0.131296 | -0.234017 | 0.094883 | 0.134334 | -0.144140 | -0.537412 | -0.280631 | -0.020020 | -0.028126 | 0.114211 | -0.187622 | -0.028582 | -0.128280 | -0.195080 | -0.160542 | -0.097916 | -0.147763 | -0.137399 | 0.177659 | -0.056272 | -0.358451 | 0.153000 | 0.033627 | -0.036912 | -0.295768 | -0.362721 | 0.017746 | -0.323436 | -0.530531 | -0.007776 | -0.157765 | -0.796654 | 0.290945 | -0.413350 | -0.054640 | 0.198538 | -0.287249 | 0.214496 | -0.064324 | 0.090781 | -0.232589 | -0.265183 | -0.251886 | -0.219194 | -0.056031 | -0.035017 | -0.114653 | -0.218287 | 0.306655 | -0.170471 | 0.018945 | -0.754508 | 0.180188 | 0.207500 | -0.079080 | -0.443078 | -0.135049 | 0.697587 | 0.189996 | -0.471130 | -0.558086 | -0.147354 | -0.077195 | -0.434535 | -0.096727 | 0.091748 | 0.113700 | -0.195010 | 0.096756 | -0.361112 | -0.574483 | -0.009378 | -0.232987 | -0.393908 | -0.192602 | -0.083266 | -0.276187 | -0.416254 | -0.320497 | -0.005439 | -0.107867 | -0.838528 | -0.363896 | -0.239209 | -0.540397 | 0.510401 | 0.052161 | -0.580379 | -0.133835 | -0.289397 | 0.265427 | -0.264489 | 0.595048 | 0.574656 | 0.464328 | -0.107824 | -0.107315 | -0.239094 | -0.168570 | -0.430431 | -0.115012 | 0.296333 | -0.266616 | 0.065201 | -0.230357 | -0.122014 | -0.245155 | 0.303565 | 0.056237 | 0.136733 | -0.534921 | 0.159647 | 0.281523 | -0.318070 | -0.171916 | 0.064884 | -0.261164 | 0.163015 | 0.057460 | -0.190698 | -0.144005 | 0.430455 | -0.313115 | 0.141877 | -0.711848 | 0.011089 | -0.067695 | 0.147941 | -0.059997 | -0.162217 | 0.376607 | 1.848225 | -0.241328 | -0.215452 | -0.365594 | -0.022543 | -0.383856 | -0.122634 | -0.398354 | -0.386030 | 0.286508 | -0.238712 | -0.287538 | -0.286893 | 0.057082 | -0.565021 | -0.105632 | -0.108148 | -0.405853 | -0.053601 | -0.677072 | 0.134160 |
# creamos la variable objetivo: survival_5years
raw_df_nki['survival_5years'] = raw_df_nki['survival'].apply(lambda x: 1 if x >= 5 else 0)
# separamos las variables clínicas de las genéticas para una mejor interpretación
# solo datos clínicos
features_to_drop = raw_df_nki.columns[13:]
clinical_df = raw_df_nki.drop(features_to_drop, axis=1)
clinical_df['survival_5years'] = raw_df_nki['survival_5years']
# solo datos genéticos
features_to_drop2 = raw_df_nki.columns[:13:]
data_gen = raw_df_nki.drop(features_to_drop2, axis=1)
data_gen.drop('survival_5years', axis=1, inplace=True)
age: Edad del paciente en el momento del diagnóstico
eventdeath: Variable que indica si el paciente está vivo o muerto
survival: Duración desde el momento de la intervención hasta la muerte
timerecurrence: Tiempo de recurrencia
chemo: Indica si el paciente recibió quimioterapia como tratamiento (sí / no)
hormonal: Si el paciente recibió terapia hormonal como tratamiento (sí / no)
amputation: Indica si se ha utilizado como tratamiento la amputación (sí / no)
histtype: Tipo histológico del cáncer
diam: Diámetro del tumor primario
posnodes: Ganglios linfáticos tomados durante la cirugía que estan afectados por el cáncer
grade: Indica el grado del cáncer (nivel 1, 2 o 3)
angioinv: Indica el grado en que el cáncer ha invadido los vasos sanguíneos o los vasos linfáticos (nivel 1, 2 o 3)
lymphinfil: Indica el grado de infiltración linfocítica (nivel 1, 2 o 3)
# observamos las categorías únicas para cada una de las variables clínicas
print(pd.Series({col:clinical_df[col].unique() for col in clinical_df}))
age [43, 48, 38, 50, 42, 47, 39, 32, 45, 31, 41, 4... eventdeath [0, 1] survival [14.817248000000001, 14.261465, 6.644764, 7.74... timerecurrence [14.817248000000001, 14.261465, 6.644764, 7.74... chemo [0, 1] hormonal [0, 1] amputation [1, 0] histtype [1, 2, 5, 7, 4] diam [25, 20, 15, 10, 18, 17, 12, 40, 45, 30, 19, 5... posnodes [0, 1, 3, 2, 4, 6, 9, 7, 5, 8, 13, 11] grade [2, 3, 1] angioinv [3, 1, 2] lymphinfil [1, 2, 3] survival_5years [1, 0] dtype: object
La parte genética del conjunto de datos contiene expresiones génicas con un total 1554 variables.
# observamos las categorías únicas para cada una de las variables genéticas
print(pd.Series({col:data_gen[col].unique() for col in data_gen}))
esr1 [-0.413955, 0.195251, 0.5961770000000001, 0.50...
G3PDH_570 [-0.9542459999999999, 0.244626, 0.082434000000...
Contig45645_RC [0.051024, -0.199602, -0.156199, -0.2060409999...
Contig44916_RC [-0.111203, -0.111397, -0.08498, -0.051775, -0...
D25272 [-0.050066, -0.135207, -0.179003, -0.049676, -...
...
Contig29014_RC [-0.038726, -0.100088, -0.099965, -0.167688, -...
Contig46616_RC [0.23785599999999998, -0.466537, 0.11015499999...
NM_000888 [-0.087631, -0.23154699999999998, -0.114298, -...
NM_000898 [-0.369153, -0.643019, 0.258495, -0.251188, -0...
AF067420 [0.15379500000000002, -0.014098, -0.1989109999...
Length: 1554, dtype: object
Durante el proceso de limpieza, hay que considerar los valores ausentes, los cuales son datos que no se almacenan para una variable en una observación en cuestión. Los datos incompletos pueden distorsionar los resultados, y es por ello que necesitamos verificar que nuestro conjunto de datos no contiene missing values.
# valores ausentes de cada atributo
raw_df_nki.isnull().sum()
age 0
eventdeath 0
survival 0
timerecurrence 0
chemo 0
..
Contig46616_RC 0
NM_000888 0
NM_000898 0
AF067420 0
survival_5years 0
Length: 1568, dtype: int64
La presencia de demasiados datos faltantes en el conjunto de datos puede afectar negativamente a la precisión y exactitud del modelo. En este caso, todos los datos están completos, tanto los clínicos como los genéticos.
A continuación, transformamos el tipo de variables según su significado para una mejor comprensión.
# renombración valores variables
clinical_df.survival_5years = clinical_df.survival_5years.astype(bool)
clinical_df.eventdeath = clinical_df.eventdeath.astype(bool)
clinical_df.chemo = clinical_df.chemo.astype(bool)
clinical_df.hormonal = clinical_df.hormonal.astype(bool)
clinical_df.amputation = clinical_df.amputation.astype(bool)
clinical_df.age = clinical_df.age.astype(float)
clinical_df.diam = clinical_df.diam.astype(float)
clinical_df.posnodes = clinical_df.posnodes.astype(float)
# dimensiones del conjunto de datos
color_print('\nDimensiones del conjunto de datos:', color='red')
print(f"{raw_df_nki.shape[0]} filas y {raw_df_nki.shape[1]} columnas")
Dimensiones del conjunto de datos:
272 filas y 1568 columnas
# función para normalizar un conjunto de datos numéricos
def to_standard (df):
ss = StandardScaler()
num_df = df[df.select_dtypes(include = np.number).columns.tolist()]
std = ss.fit_transform(num_df)
return pd.DataFrame(std, index = num_df.index, columns = num_df.columns)
# boxplot de los atributos clínicos numéricos normalizados
plt.figure(figsize=(10, 5))
plt.title('Distribución de los atributos clínicos numéricos normalizados', fontsize=12)
sns.boxplot(x="value", y="variable", data=pd.melt(to_standard(clinical_df)), palette ='magma')
plt.xlabel('valores normalizados', size=10)
plt.ylabel('', size=10)
b, t = plt.xlim()
b += 0.5
t -= 0.5
plt.xlim(b, t)
plt.show()
# número de valores atípicos en cada atributo clínico
Q1 = clinical_df.quantile(0.25)
Q3 = clinical_df.quantile(0.75)
IQR = Q3 - Q1
((clinical_df < (Q1 - 1.5 * IQR)) | (clinical_df > (Q3 + 1.5 * IQR))).sum().sort_values(ascending = False).head(7)
lymphinfil 49 survival_5years 48 hormonal 36 histtype 18 posnodes 14 survival 2 age 2 dtype: int64
Por lo que respecta a la distribución de los datos numéricos, observamos como algunas características se distribuyen normalmente (como diam), mientras que otras estan sesgadas con muchos valores atípicos (como lymphinfil). Aun así, teniendo en cuenta la importancia de los valores atípicos en los datos de salud, decidimos mantenerlos.
# histogramas de densidad
fig = plt.figure(figsize = (20, 10))
j = 0
num_clinical_columns= list(clinical_df.select_dtypes(include=['float64']).columns)
for i in raw_df_nki[num_clinical_columns].columns:
plt.subplot(2, 3, j+1)
j += 1
sns.distplot(clinical_df[i][clinical_df['survival_5years']==0], color='darkviolet', label = 'survived',bins=15)
sns.distplot(clinical_df[i][clinical_df['survival_5years']==1], color='darkturquoise', label = 'died',bins=15)
plt.legend(loc='best')
plt.xlabel('')
plt.title(i)
fig.suptitle('Distribución atributos numéricos según la supervivencia', fontsize=20)
fig.tight_layout()
fig.subplots_adjust(top=0.92)
plt.show()
# boxplots
fig = plt.figure(figsize = (20, 10))
j = 0
for i in raw_df_nki[num_clinical_columns].columns:
plt.subplot(2, 3, j+1)
j += 1
sns.boxplot(x=i, y='survival_5years', orient='h', data=clinical_df, palette=('darkviolet','darkturquoise'))
plt.yticks([-0.5, 0, 1, 1.5], ['','Survived', 'Died',''])
plt.ylabel('')
plt.xlabel('')
plt.title(i)
fig.suptitle('Distribución atributos numéricos según la supervivencia', fontsize=20)
fig.tight_layout()
fig.subplots_adjust(top=0.92)
plt.show()
# resumen estadístico atributos numéricos
raw_df_nki[num_clinical_columns].describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| age | 272.0 | 44.047794 | 5.464538 | 26.000000 | 40.750000 | 45.000000 | 49.000000 | 53.000000 |
| survival | 272.0 | 8.080609 | 3.904874 | 0.711841 | 5.499738 | 7.359343 | 10.512662 | 18.340862 |
| timerecurrence | 272.0 | 7.250433 | 4.177462 | 0.271047 | 4.389459 | 6.950034 | 9.986311 | 18.340862 |
| diam | 272.0 | 22.529412 | 8.703345 | 2.000000 | 15.000000 | 20.000000 | 29.250000 | 50.000000 |
| posnodes | 272.0 | 1.341912 | 2.108848 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 13.000000 |
Para cada una de las variables numéricas, observamos un histograma de densidad y un boxplot con la finalidad de resumir y visualizar la distribución de dichas características. El histograma de densidad proporciona una descripción más detallada de la forma y simetría de la distribución de los datos, mientras que el boxplot resalta las medidas de posición y dispersión, además de identificar valores atípicos. Ambas visualizaciones son complementarias y útiles para explorar y entender las características de un conjunto de datos.
A simple vista, observamos como las variables age y diam muestran diferencias entre los pacientes que sobreviven 5 años y los que no. Tanto en los histogramas como los diagramas de cajas vemos como los pacientes que no sobreviven tienden a ser mayores y a tener un tamaño del tumor más pequeño.
En cambio, la variable posnodes, la cual se encuentra sesgada a la derecha, no muestra una clara diferencia entre ambos grupos. Aun así, observamos como el rango intercuartílico es mayor para los pacientes que sobreviven, y el número de valores atípicos es mayor en los pacientes que mueren.
Por lo que respecta a la variable survival, observamos una clara diferenciación entre los dos grupos. Esto es evidente dado que la variable objetivo survival_5years se ha creado teniendo en cuenta la supervivencia en meses. Los pacientes que seguian vivos a los cinco años de ser diagnosticados, se les ha asignado la categoría survived, mientras que los pacientes que murieron antes se les ha asignado la categoría died. Dicha separación queda reflejada tanto en el histograma como en el diagrama de cajas.
La variable timerecurrence se distribuye de forma muy similar a survival, ya que el tiempo de recurrencia es generalmente el mismo que el tiempo de supervivencia cuando el paciente no muere de cáncer. Teniendo en cuenta esto, nos planteamos la posible afectación de esta variable a la precisión del modelo. Si en el análisis de correlación queda plasmada dicha redundancia, eliminaremos la variable.
# Distribución de los atributos clínicos categóricos según la supervivencia
fig = plt.figure(figsize = (20, 10))
j = 0
cat_clinical_columns = list(clinical_df.select_dtypes(include=['bool','int64','object']).columns)
cat_clinical_columns.remove('survival_5years')
for i in cat_clinical_columns:
plt.subplot(2, 4, j+1)
j += 1
# cálculo de los porcentajes
counts = clinical_df[i].value_counts().reset_index().rename(columns={i: 'count', 'index': i})
survival_counts = clinical_df.groupby([i, 'survival_5years']).size().reset_index(name='survival_count')
all_df = survival_counts.merge(counts)
survival_counts['total']= all_df['survival_count'] / len(clinical_df['survival_5years']) * 100
# gráfico
sns.barplot(x=i, y='total', hue='survival_5years', data=survival_counts, palette=('darkviolet', 'darkturquoise'))
plt.ylabel('Percentage of patients')
plt.xlabel('')
plt.title(i)
plt.xticks(rotation=90)
plt.ylim(0, 100)
fig.suptitle('Distribución de los atributos clínicos categóricos según la supervivencia',fontsize=20)
fig.tight_layout()
fig.subplots_adjust(top=0.92)
plt.show()
# resumen estadístico atributos categóricos
clinical_df[cat_clinical_columns].astype('category').describe().T
| count | unique | top | freq | |
|---|---|---|---|---|
| eventdeath | 272 | 2 | False | 195 |
| chemo | 272 | 2 | False | 165 |
| hormonal | 272 | 2 | False | 236 |
| amputation | 272 | 2 | False | 152 |
| histtype | 272 | 5 | 1 | 254 |
| grade | 272 | 3 | 3 | 106 |
| angioinv | 272 | 3 | 1 | 169 |
| lymphinfil | 272 | 3 | 1 | 223 |
Los gráficos de barras son una forma común y efectiva de visualizar y comparar datos categóricos o discretos. Estos gráficos utilizan barras rectangulares para representar la frecuencia, el conteo, la proporción u otra medida asociada con diferentes categorías. En esta ocasión los usamos para comparar los porcentajes de las categorías de la variable en cuestión, diferenciando la categoría survived y la categoría died de la variable _survival5years.
Antes de nada, hay que tener en cuenta que el conjunto de pacientes que sobrevive 5 años es 5 veces mayor que el grupo de pacientes que no sobrevive 5 años, y por esta razon todas las categorías tienen un mayor porcentaje de pacientes vivos.
De forma concreta, observamos cierta tendencia en las variables grade y lymphinfil. A mayor grado de cáncer, mayor número de pacientes que mueren antes de llegar a los 5 años del diagnóstico. A mayor grado de infiltración linfocítica, menor número de pacientes que no sobreviven 5 años.
La categoría más representada del resto de variables puede encontrarse en el resumen estadístico mostrado.
# características medias de un paciente
print("Edad promedio: " + "%.3f" %np.mean(clinical_df['age']))
print("Diámetro medio del tumor: " + "%.3f" %np.mean(clinical_df['diam']))
print("Grado medio: " + "%.3f" %np.mean(clinical_df['grade']))
print("Probabilidad de supervivencia 5 años: "+ "%.3f" %(clinical_df["survival_5years"].value_counts()/clinical_df["survival_5years"].count()*100).iloc[0])
Edad promedio: 44.048 Diámetro medio del tumor: 22.529 Grado medio: 2.129 Probabilidad de supervivencia 5 años: 82.353
Según el conjunto de datos, la paciente promedio de cáncer de mama es una mujer de 44 años con un tamaño tumoral promedio de 22 mm con 2 ganglios linfáticos examinados positivos. La paciente tiene una probabilidad del 82% de sobrevivir al menos 5 años después del diagnóstico.
A continuación, utilizamos el diagrama de Venn para mostrar como se distribuyen los tratamientos que las pacientes han recibido.
# creamos subconjuntos para cada combinación de tratamientos
chemo = raw_df_nki[(raw_df_nki["chemo"]==True) & (raw_df_nki["amputation"]==False) & (raw_df_nki["hormonal"]==False)]
amputation = raw_df_nki[(raw_df_nki["chemo"]==False) & (raw_df_nki["amputation"]==True) & (raw_df_nki["hormonal"]==False)]
hormonal = raw_df_nki[(raw_df_nki["chemo"]==False) & (raw_df_nki["amputation"]==False) & (raw_df_nki["hormonal"]==True)]
chemo_amputation = raw_df_nki[(raw_df_nki["chemo"]==True) & (raw_df_nki["amputation"]==True) & (raw_df_nki["hormonal"]==False)]
amputation_hormonal = raw_df_nki[(raw_df_nki["chemo"]==False) & (raw_df_nki["amputation"]==True) & (raw_df_nki["hormonal"]==True)]
hormonal_chemo = raw_df_nki[(raw_df_nki["chemo"]==True) & (raw_df_nki["amputation"]==False) & (raw_df_nki["hormonal"]==True)]
all_3 = raw_df_nki[(raw_df_nki["chemo"]==True) & (raw_df_nki["amputation"]==True) & (raw_df_nki["hormonal"]==True)]
any_3 = raw_df_nki[(raw_df_nki["chemo"]==False) & (raw_df_nki["amputation"]==False) & (raw_df_nki["hormonal"]==False)]
# gráfico teniendo en cuenta el tamaño de los círculos y el color en relación con el tamaño de cada subconjunto
fig, ax = plt.subplots(figsize=(10,10))
v = venn3(subsets=[len(chemo), len(amputation), len(chemo_amputation), len(hormonal),len(hormonal_chemo), len(amputation_hormonal), len(all_3)], set_labels=("chemo", "amputation", "hormonal"), ax=ax, alpha=0.6, set_colors=sns.diverging_palette(300, 145, s=60))
ax.set_title("Número de pacientes por tratamiento", size=15)
plt.show()
print(f'Pacientes sin ningún tratamiento administrado: {len(any_3)}')
Pacientes sin ningún tratamiento administrado: 87
La mayoría de los pacientes reciben el tratamiento único amputación, mientras que el tratamiento único administrado con menor frecuencia es el hormonal. Por lo que respecta a la combinación de tratamientos, la más administrada es la quimioterapia junto con la amputación. La quimioterapia con el tratamiento hormonal se ha administrado la misma cantidad de veces que la amputación con el tratamiento hormonal. La combinación de los tres tratamientos se ha administrado a solo 11 pacientes. Mencionar también que hay 87 pacientes que no han recibido ninguno de estos tratamientos.
# agrupamos los datos por las variables y calculamos la tasa de supervivencia
grouped = clinical_df.groupby(['chemo','amputation', 'hormonal'])['survival_5years'].mean().reset_index()
grouped.columns = ['chemo','amputation', 'hormonal', 'survival_rate']
grouped
| chemo | amputation | hormonal | survival_rate | |
|---|---|---|---|---|
| 0 | False | False | False | 0.839080 |
| 1 | False | False | True | 0.888889 |
| 2 | False | True | False | 0.754098 |
| 3 | False | True | True | 0.625000 |
| 4 | True | False | False | 0.812500 |
| 5 | True | False | True | 1.000000 |
| 6 | True | True | False | 0.850000 |
| 7 | True | True | True | 1.000000 |
# survival_rate promedio
print("Proporción de supervivencia promedio:", ("%.3f" %np.mean(clinical_df["survival_5years"])))
Proporción de supervivencia promedio: 0.824
Estos datos muestran que las pacientes que recibieron todos los tratamientos tienen una tasa de supervivencia del 100%, mientras que las pacientes que no recibieron ninguno tienen una tasa de supervivencia ligeramente superior a la media.
# gráfico de correlaciones
fig, axs = plt.subplots(figsize = (15, 15))
mask = np.triu(np.ones_like(clinical_df.corr(), dtype = np.bool))
sns.heatmap(clinical_df.corr(), ax = axs, mask = mask, cmap=sns.diverging_palette(250, 300, s=80, l=55, as_cmap=True),)
plt.title('Correlaciones de atributos clínicos')
b, t = plt.ylim()
b += 0.5
t -= 0.5
plt.ylim(b, t)
plt.show()
El coeficiente de correlación oscila entre -1 (fuerte correlación negativa) y 1 (fuerte correlación positiva).
Como podemos observar, la mayor correlación corresponde a los atributos timerecurrence y survival como hemos intuido préviamente. El tiempo de timerecurrence es generalmente el mismo que el tiempo de survival cuando el paciente no muere de cáncer. Teniendo en cuenta esto, nos planteamos la posible afectación de esta variable a la precisión del modelo, y para evitar dicha redundancia eliminamos la variable timerecurrence del conjunto de datos.
De la misma manera, survival y _survival5years están correlacionadas postivamente por el hecho que se ha generado una variable a partir de la otra. Para evitar una posible afectación al modelo, eliminamos la variable survival.
eventdeath y _survival5years están correlacionas negativamente, y decidimos eliminar la variable eventdeath.
Mencionar que la decisión de eliminar una de las dos variables se ha tomado basándonos en la correlación con la variable objetivo. Se ha eliminado la que menor correlación tenía con dicha variable (_survival5years).
# eliminación de variables correlacionadas
raw_df_nki = raw_df_nki.drop(['timerecurrence','eventdeath','survival'], axis=1)
clinical_df = clinical_df.drop(['timerecurrence','eventdeath','survival'], axis=1)
# gráfico final de correlaciones
fig, axs = plt.subplots(figsize = (15, 15))
mask = np.triu(np.ones_like(clinical_df.corr(), dtype = np.bool))
sns.heatmap(clinical_df.corr(), ax = axs, mask = mask, cmap=sns.diverging_palette(250, 300, s=80, l=55, as_cmap=True),)
plt.title('Correlaciones de atributos clínicos')
b, t = plt.ylim()
b += 0.5
t -= 0.5
plt.ylim(b, t)
plt.show()
Aunque aún podemos observar ciertas correlaciones, ya no son tan fuertes como anteriormente (observar que la leyenda de colores tiene límites menores).
A continuación, mostramos únicamente las correlaciones de las variables clínicas con la variable objetivo survival_5years.
# matriz de correlación con la variable objetivo 'survival_5years'
target_cor_matrix = clinical_df.corr()[['survival_5years']].sort_values('survival_5years',ascending=True)
# heatmap
plt.figure(figsize = (3, 15))
ax=sns.heatmap(target_cor_matrix, annot=True, cmap=sns.diverging_palette(250, 300, s=80, l=55, as_cmap=True),linewidth=0.3,fmt=".2f", vmax=1.00, vmin=-1.00)
ax.set_title(label='correlaciones de survival_5years con el resto de variables\n', fontsize=12)
ax.set(ylim=(0, target_cor_matrix.shape[0]))
plt.show()
En esta ocasión, todas las variables clínicas tienen cierta correlación con la variable objetivo _survival5years. Las características más correlacionadas positivamente son: age, chemo y hormonal, con 0.14, 0.08 y 0.07, respectivamente. Las características más correlacionadas negativamente son: grade, diam y lymphinfil, con -0.37, -0.22 y -0.15, respectivamente.
# separamos las variables clínicas de las genéticas para una mejor interpretación
# solo datos genéticos
features_to_drop2 = raw_df_nki.columns[:10:]
data_gen = raw_df_nki.drop(features_to_drop2, axis=1)
# binarizamos la variable objetivo
data_gen['survival_5years'] = raw_df_nki['survival_5years'].replace([False,True],[0,1]).astype(int)
# estadísticas de los datos genéticos
data_gen.drop('survival_5years',axis=1).describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| esr1 | 272.0 | -0.253044 | 0.559309 | -1.511655 | -0.581590 | -0.084256 | 0.152075 | 0.596177 |
| G3PDH_570 | 272.0 | -0.025154 | 0.626507 | -1.951243 | -0.165442 | 0.056432 | 0.213871 | 1.897414 |
| Contig45645_RC | 272.0 | -0.042121 | 0.376843 | -2.000000 | -0.160641 | -0.049159 | 0.099495 | 2.000000 |
| Contig44916_RC | 272.0 | -0.019142 | 0.301998 | -2.000000 | -0.106249 | -0.029375 | 0.041888 | 2.000000 |
| D25272 | 272.0 | 0.037009 | 0.347107 | -1.132837 | -0.077732 | 0.003066 | 0.064462 | 2.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Contig29014_RC | 272.0 | -0.015141 | 0.323474 | -2.000000 | -0.099996 | -0.002994 | 0.074571 | 2.000000 |
| Contig46616_RC | 272.0 | -0.057222 | 0.297915 | -0.871728 | -0.253693 | -0.052311 | 0.166774 | 0.591880 |
| NM_000888 | 272.0 | -0.038441 | 0.246020 | -0.593263 | -0.185253 | -0.071930 | 0.100221 | 0.993960 |
| NM_000898 | 272.0 | -0.240416 | 0.482092 | -1.679470 | -0.579311 | -0.213541 | 0.116691 | 1.298576 |
| AF067420 | 272.0 | -0.252884 | 0.472775 | -2.000000 | -0.554709 | -0.254435 | 0.051704 | 0.988492 |
1554 rows × 8 columns
# Visualización de la expresión génica en un heatmap
fig, axs = plt.subplots(figsize = (30, 10))
sns.heatmap(data_gen.drop('survival_5years',axis=1), ax = axs, cmap=sns.diverging_palette(250, 300, s=80, l=55, as_cmap=True))
plt.title('Heatmap expresión génica')
b, t = plt.ylim()
b += 0.5
t -= 0.5
plt.ylim(b, t)
plt.show()
En este tipo de gráficos, cada fila representa una muestra y cada columna representa un gen. El color y la intensidad de las cajas se utilizan para representar cambios (no valores absolutos) de la expresión génica. En esta ocasión, el lila representa genes regulados hacia arriba y el azul representa genes regulados hacia abajo. El blanco representa la expresión inalterada.
Aunque la gran cantidad de datos dificulte la comprensión del gráfico, no se pretende entrar mucho en detalle. Simplemente, pretendemos mostrar que es un tipo de gráfico para mostrar la información que disponemos.
# distribución atributos genéticos según la supervivencia --> solo 100 de los 1554...
fig = plt.figure(figsize = (20, 40))
j = 0
num_gen_columns= list(data_gen.select_dtypes(include=['float64']).columns)[:100]
for i in num_gen_columns:
plt.subplot(20, 5, j+1)
j += 1
sns.distplot(data_gen[i][data_gen['survival_5years']==1], color='r', label = 'died',bins=15)
sns.distplot(data_gen[i][data_gen['survival_5years']==0], color='g', label = 'survived',bins=15)
plt.legend(loc='best')
plt.xlabel('')
plt.title(i)
fig.suptitle('Distribución atributos genéticos según la supervivencia', fontsize=20)
fig.tight_layout()
fig.subplots_adjust(top=0.92)
plt.show()
Aunque distribución de los datos en las dos clases de supervivencia es muy similar para la mayoría de genes, observamos diferencias destacables en alguno de ellos. Por ejemplo: ers1, Contig56678_RC o NM_001609.
Mencionar que solo se han representado 100 de los 1554 genes que contiene el conjunto de datos.
# número de valores atípicos para cada gen
Q1 = data_gen.drop('survival_5years',axis=1).quantile(0.25)
Q3 = data_gen.drop('survival_5years',axis=1).quantile(0.75)
IQR = Q3 - Q1
((data_gen.drop('survival_5years',axis=1) < (Q1 - 1.5 * IQR)) | (data_gen.drop('survival_5years',axis=1) > (Q3 + 1.5 * IQR))).sum().sort_values(ascending = False).head()
NM_002509 55 NM_001942 53 NM_000509 51 G3PDH_570 48 Contig44191_RC 47 dtype: int64
Los genes que tienen más valores atípicos son: _NM002509, _NM001942 y _NM000509, en concreto, más de 50.
A continuación, mostramos como se correlacionan los genes con la variable objetivo _survival5years.
# gráfico correlación genes y survival_5years
fig, ax = plt.subplots(figsize=(10,4))
corrs=[]
for col in data_gen.columns:
corrs.append(data_gen[[col,'survival_5years']].corr()['survival_5years'][col])
corrs.pop(-1)
ax.hist(corrs, bins=25, color = 'darkturquoise')
ax.set_xlabel("Correlación")
ax.set_ylabel("Número de genes")
ax.set_title("Histograma correlación genes con supervivencia 5 años", size=12)
plt.show()
print("Correlación positiva más alta: " + "%.3f" %max(corrs))
print("Correlación negativa más alta: " + "%.3f" %min(corrs))
print("Correlación media: " + "%.3f" %np.mean(corrs))
Correlación positiva más alta: 0.373 Correlación negativa más alta: -0.343 Correlación media: 0.011
El gráfico de la correlación entre la variable objetivo y las características genéticas muestra como una distribución bastante normal. Aunque la mayoría de las características no se correlacionan, algunas de ellas estan correlacionadas con un valor superior a 0.3 absoluto.
# Correlaciones atributos genéticos
fig, axs = plt.subplots(figsize = (100, 100))
mask = np.triu(np.ones_like(data_gen.corr(), dtype = np.bool))
sns.heatmap(data_gen.corr(), ax = axs, mask=mask, cmap=sns.diverging_palette(250, 300, s=80, l=55, as_cmap=True))
plt.title('Correlaciones atributos genéticos')
b, t = plt.ylim()
b += 0.5
t -= 0.5
plt.ylim(b, t)
plt.show()
Sin duda alguna, este gráfico no es interpretable...
De forma más precisa, nos centramos en mostrar únicamente aquellas variables genéticas que están más correlacionadas en valor absoluto con la variable objetivo _survival5years.
# correlaciones de las variables genéticas con la variable objetivo 'survival_5years'
target_cor_matrix = data_gen.corr()[['survival_5years']].sort_values('survival_5years',ascending=True)
target_cor_matrix
| survival_5years | |
|---|---|
| NM_001333 | -0.342691 |
| NM_018410 | -0.336975 |
| NM_006096 | -0.331805 |
| Contig57584_RC | -0.329595 |
| NM_005733 | -0.326794 |
| ... | ... |
| AL137566 | 0.359258 |
| AL049265 | 0.369567 |
| esr1 | 0.372706 |
| NM_000125 | 0.372706 |
| survival_5years | 1.000000 |
1555 rows × 1 columns
Las variables genéticas más correlacionadas positivamente son _NM001333, _NM018410 y _NM006096, mientras que las más correlacionadas negativamente son 0.372706, 0.372706 y 0.369567.
Teniendo en cuenta la alta dimensionalidad de estos datos, nos planteamos reducirla aplicando métodos de reducción de la dimensionalidad al conjunto de datos genéticos.
El objetivo es reducir el conjunto de atributos genéticos a un nuevo conjunto con menos dimensiones, que contengan, sin embargo, la máxima información posible presente en los atributos originales. Para hacerlo parecido al otro conjunto de datos de este proyecto, definimos el número de componentes a 100.
# aplicamos PCA
X = data_gen.drop(['survival_5years'],axis=1).values
pca = PCA(n_components=100)
# normalizar y transformar los datos
X_pca = pca.fit_transform(preprocessing.scale(X))
print('Dimensiones conjunto de datos original: {}'.format(X.shape))
print('Dimensiones conjunto de datos transformado: {}'.format(X_pca.shape))
Dimensiones conjunto de datos original: (272, 1554) Dimensiones conjunto de datos transformado: (272, 100)
# definimos el nombre de las nuevas variables: C (de componente) + el número
name_col=['C'+str(num) for num in range(0,100)]
# creamos un conjunto de datos que incluya la información genética reducida junto con los datos clínicos preprocesados
resultado = pd.concat([clinical_df,pd.DataFrame(X_pca,columns=name_col)],axis=1)
resultado
| age | chemo | hormonal | amputation | histtype | diam | posnodes | grade | angioinv | lymphinfil | survival_5years | C0 | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 | C12 | C13 | C14 | C15 | C16 | C17 | C18 | C19 | C20 | C21 | C22 | C23 | C24 | C25 | C26 | C27 | C28 | C29 | C30 | C31 | C32 | C33 | C34 | C35 | C36 | C37 | C38 | C39 | C40 | C41 | C42 | C43 | C44 | C45 | C46 | C47 | C48 | C49 | C50 | C51 | C52 | C53 | C54 | C55 | C56 | C57 | C58 | C59 | C60 | C61 | C62 | C63 | C64 | C65 | C66 | C67 | C68 | C69 | C70 | C71 | C72 | C73 | C74 | C75 | C76 | C77 | C78 | C79 | C80 | C81 | C82 | C83 | C84 | C85 | C86 | C87 | C88 | C89 | C90 | C91 | C92 | C93 | C94 | C95 | C96 | C97 | C98 | C99 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 43.0 | False | False | True | 1 | 25.0 | 0.0 | 2 | 3 | 1 | True | -12.364424 | -4.064729 | 0.820999 | 1.035345 | -0.067892 | 4.544601 | 1.567334 | 1.610498 | 1.758890 | 2.770283 | -0.684424 | -0.139539 | 8.262122 | -6.858509 | 1.036035 | -5.569854 | -0.655849 | -1.662480 | 5.044525 | -2.382108 | 0.702962 | 0.116429 | 3.366905 | -0.085129 | -4.162193 | 1.828367 | 1.786278 | -1.712223 | 1.647665 | -0.993592 | 1.199254 | -1.451720 | -1.886101 | 2.474643 | 4.021143 | -1.996666 | 3.033532 | 0.300835 | -1.925778 | -1.198536 | 0.224366 | 2.021819 | -0.080066 | -1.285813 | 0.430792 | 0.766748 | -6.058970 | -0.001291 | -2.995210 | 1.352337 | -3.235693 | 2.380885 | -0.809578 | 2.163060 | 0.280692 | -0.826362 | -1.652951 | -3.172382 | -0.679624 | -1.070765 | -2.612715 | -2.034535 | 2.224217 | 4.283467 | -0.131707 | -0.073946 | -2.752752 | 1.376621 | -2.058584 | 3.404967 | 1.277073 | -0.426300 | 1.928754 | -2.429654 | -1.874388 | 2.129146 | 1.893931 | 3.473282 | -4.474198 | 2.524202 | 1.538636 | -0.647295 | 1.189870 | 2.125407 | -1.358348 | -2.173841 | 0.065039 | 1.512529 | -1.288458 | -0.128130 | -0.717715 | 0.103813 | -1.727946 | 2.826620 | 0.778762 | -4.128197 | 0.572071 | -0.336498 | 0.298645 | -1.087107 |
| 1 | 48.0 | False | False | False | 1 | 20.0 | 0.0 | 3 | 3 | 1 | True | -11.025288 | -3.795678 | -1.737388 | -1.322813 | -2.156423 | -5.912853 | 4.186283 | -3.576680 | -6.426347 | 3.159344 | -4.830573 | 0.191842 | -6.871924 | 3.057797 | 0.085797 | 2.746071 | -0.093943 | -0.223901 | -1.492098 | -2.503628 | -4.813850 | 4.188228 | -1.277441 | 2.868077 | -2.015915 | -1.172454 | 2.462641 | 1.707223 | -0.276116 | -0.938516 | 2.473477 | 0.435089 | -0.440974 | -1.297366 | -3.635891 | -2.558229 | 0.413363 | -0.674923 | 1.950665 | -3.004352 | 3.058390 | -1.201817 | 0.595289 | -0.771219 | -3.004933 | 0.390489 | -0.065892 | 1.334957 | 0.656748 | 0.451429 | 1.196638 | 1.375124 | 1.274605 | 0.442187 | -1.744000 | 2.319449 | 1.570544 | -0.576949 | -0.169359 | 1.010265 | -1.801629 | 0.613879 | -1.738218 | -1.387811 | -0.321733 | 0.806535 | -3.655466 | 1.454867 | 0.208481 | 0.332798 | -3.041917 | 0.067426 | -0.312646 | -0.009697 | -0.530753 | 2.392212 | 0.715594 | -2.067933 | -0.074921 | 0.667708 | -0.130271 | -0.979942 | -2.789469 | 0.234677 | -0.261317 | 0.153757 | 0.706683 | -0.857707 | 0.957768 | 1.525501 | -0.905291 | -0.026384 | -0.699413 | 0.063673 | 1.674424 | 2.713216 | -2.436896 | 0.255662 | -2.036471 | 1.993406 |
| 2 | 38.0 | False | False | False | 1 | 15.0 | 0.0 | 2 | 1 | 1 | True | -13.135728 | -3.058893 | -0.030498 | -0.483964 | 5.336800 | -4.131843 | 2.341435 | 2.076521 | -4.177174 | -2.419014 | -4.288299 | -0.780529 | -2.261387 | 1.584077 | -2.708174 | 2.500659 | 1.529401 | -3.909545 | 2.920720 | -5.459583 | -2.953400 | 4.631125 | 0.379629 | 3.790653 | 1.884096 | -1.682804 | -0.168979 | -0.884917 | 1.157920 | -1.356694 | 0.194097 | -2.089105 | 3.192543 | 1.130828 | -2.292690 | -1.153680 | -3.642614 | -0.432995 | 0.676602 | -3.474911 | 3.859707 | -2.519921 | 1.197221 | 0.913977 | -2.366005 | 1.427493 | 1.708330 | 3.384200 | 0.625871 | -0.729195 | -1.552081 | 0.265213 | 1.070386 | 2.264964 | -1.888461 | 0.963924 | 0.887235 | -1.761404 | -1.412520 | -1.149235 | -1.295081 | 1.364491 | -1.406476 | 2.268617 | -1.215507 | -0.696012 | -3.566278 | -0.506186 | -2.165754 | -2.784616 | -1.679842 | 1.477627 | 2.600854 | -1.869495 | -0.080223 | 0.377087 | -1.106674 | 0.717518 | -2.270098 | 0.413814 | -1.400191 | -0.048956 | -2.033111 | -2.281937 | 2.921777 | -0.503249 | -0.023687 | -0.425255 | 1.111113 | -0.954354 | 0.016539 | -0.010903 | -1.853894 | 1.273045 | -0.233435 | 1.944929 | 1.150861 | -1.759681 | -1.482328 | -2.100301 |
| 3 | 50.0 | False | True | False | 1 | 15.0 | 1.0 | 2 | 3 | 1 | True | -14.455227 | 2.617116 | -1.153033 | 8.622665 | -0.448925 | 3.749880 | 13.918670 | 0.482140 | -8.999689 | 1.818286 | -2.343649 | -1.532074 | -1.866385 | -2.336911 | -0.892286 | -0.753035 | 0.341578 | 3.131392 | 4.592891 | 0.113318 | -4.547616 | -2.136747 | 4.384115 | -0.395372 | -3.154138 | -2.728470 | -0.351291 | 3.093758 | 0.108664 | 3.353144 | 3.502612 | 3.197300 | -4.712087 | -2.671823 | 1.479209 | -0.474093 | 5.584539 | -2.169790 | 2.345478 | -3.533916 | -0.845253 | 0.434199 | 3.264485 | 0.401275 | -2.255432 | -0.446585 | 1.775820 | 1.256498 | -0.618815 | -1.409274 | -2.544269 | 1.970357 | -1.743012 | 1.464230 | -1.024151 | 4.343768 | 1.137649 | -2.198934 | -3.796692 | 1.408697 | 0.407678 | 1.792810 | -1.773669 | -1.803171 | -0.533765 | 1.002971 | 0.028192 | 0.932722 | -1.057666 | -1.643304 | -1.221795 | 1.606162 | 3.794524 | 0.741215 | 0.527746 | -0.887023 | 1.834075 | 0.296867 | 0.595999 | 1.473187 | -0.528779 | 0.141295 | 2.781818 | -0.262928 | -0.958000 | 1.232112 | -0.986775 | -0.016734 | 1.114084 | -1.788183 | 0.410500 | -0.433427 | 0.367555 | -1.553780 | -0.658228 | 2.309344 | 0.263229 | 0.262259 | 0.189068 | -0.785406 |
| 4 | 38.0 | False | False | True | 1 | 15.0 | 0.0 | 2 | 2 | 1 | True | -6.153006 | 10.228359 | -0.097261 | 0.745455 | -4.467332 | -6.573353 | 5.757971 | -2.411389 | 1.141044 | -4.305214 | -2.608953 | -2.871912 | -3.401214 | 4.542830 | -4.715130 | 3.188017 | 3.526002 | -0.731473 | 0.954756 | 0.607979 | -3.035999 | -2.561234 | 5.589953 | -2.304925 | -2.793639 | 1.986980 | 0.817757 | -1.467478 | -0.535497 | -1.760722 | -2.203703 | 0.497792 | -1.985938 | 1.394848 | -0.836274 | -1.786106 | 2.324860 | -1.342803 | -1.755046 | -1.351255 | -0.091721 | 2.175480 | 1.315634 | 0.847168 | -1.259126 | 0.767275 | 2.734391 | -3.072760 | -1.692247 | -2.247539 | -0.885422 | 0.353747 | 1.178807 | 1.397751 | 1.052956 | -1.243686 | 0.140542 | 1.653296 | -1.014423 | 1.368856 | -0.702628 | -0.564275 | -1.678064 | -2.894154 | 1.018958 | 1.834715 | -0.566030 | 2.439620 | -0.241855 | -1.738796 | 0.518499 | -1.320284 | 0.122323 | -0.269679 | 0.701156 | -1.822909 | 1.142053 | 0.298605 | 0.215996 | 1.394866 | -0.011386 | 0.873581 | -0.376201 | -0.726424 | -2.072275 | 1.951220 | 1.508748 | -0.814256 | -0.241488 | -2.773654 | 0.015037 | -1.277557 | -0.321950 | -0.396099 | 0.866775 | -0.638567 | -0.759740 | -1.673136 | -1.951346 | 1.616051 |
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| 271 | 52.0 | False | False | True | 1 | 30.0 | 0.0 | 2 | 1 | 1 | False | 11.417361 | -10.189544 | 3.971728 | -14.470247 | -2.255700 | 7.482361 | -0.166704 | -7.162185 | -1.603126 | 0.443521 | 5.280922 | -0.922374 | -11.032332 | -2.547131 | -2.228878 | -2.104238 | -2.541941 | 5.982972 | -0.155089 | 5.213937 | -2.816525 | -4.241862 | -2.945541 | 1.652549 | 3.839460 | -2.658585 | -10.398803 | -4.907622 | -5.436497 | 3.347300 | 2.714957 | -0.410557 | -5.231900 | 0.468960 | 5.290059 | -3.455059 | 0.214522 | -1.431989 | 1.741349 | 5.962390 | 1.864228 | -7.258048 | 0.614993 | 4.677699 | 0.533422 | 2.159149 | -1.185993 | -3.435878 | -1.046275 | 0.930182 | 0.873974 | -1.465676 | -0.824100 | 5.662214 | -1.195737 | 2.167766 | 4.746288 | 0.161812 | 12.722291 | 4.866895 | -3.611601 | -2.766190 | 0.844800 | -0.118384 | 1.178359 | 3.925902 | -1.163907 | -2.401569 | 0.737321 | 4.641622 | 1.285971 | 2.780455 | 3.393091 | -5.663202 | 8.084987 | 0.375934 | 1.472424 | 6.126872 | -1.835175 | -0.308313 | -5.369004 | 1.889241 | -0.059379 | 1.359634 | -1.177022 | 1.394756 | -1.001871 | 0.989258 | -5.362291 | 0.761261 | -0.798388 | -5.008434 | -4.689224 | -1.582665 | 2.784633 | 1.733249 | -1.788877 | 0.443515 | -2.258765 | 0.344887 |
272 rows × 111 columns
# guardamos el conjunto de datos resultante
resultado.to_csv('{}/data/taules/NKI_PCA.csv'.format(global_path), header=True, sep="\t",index=False)